Eui Hoon Lee
Full Text Available Imperviousness has increased due to urbanization, as has the frequency of extreme rainfall events by climate change. Various countermeasures, such as structural and nonstructural measures, are required to prepare for these effects. Flood forecasting is a representative nonstructural measure. Flood forecasting techniques have been developed for the prevention of repetitive flood damage in urban areas. It is difficult to apply some flood forecasting techniques using training processes because training needs to be applied at every usage. The other flood forecasting techniques that use rainfall data predicted by radar are not appropriate for small areas, such as single drainage basins. In this study, a new flood forecasting technique is suggested to reduce flood damage in urban areas. The flood nomograph consists of the first flooding nodes in rainfall runoff simulations with synthetic rainfall data at each duration. When selecting the first flooding node, the initial amount of synthetic rainfall is 1 mm, which increases in 1 mm increments until flooding occurs. The advantage of this flood forecasting technique is its simple application using real-time rainfall data. This technique can be used to prepare a preemptive response in the process of urban flood management.
Abou Rjeily, Yves; Abbas, Oras; Sadek, Marwan; Shahrour, Isam; Hage Chehade, Fadi
Urbanization activity and climate change increase the runoff volumes, and consequently the surcharge of the urban drainage systems (UDS). In addition, age and structural failures of these utilities limit their capacities, and thus generate hydraulic operation shortages, leading to flooding events. The large increase in floods within urban areas requires rapid actions from the UDS operators. The proactivity in taking the appropriate actions is a key element in applying efficient management and flood mitigation. Therefore, this work focuses on developing a flooding forecast system (FFS), able to alert in advance the UDS managers for possible flooding. For a forecasted storm event, a quick estimation of the water depth variation within critical manholes allows a reliable evaluation of the flood risk. The Nonlinear Auto Regressive with eXogenous inputs (NARX) neural network was chosen to develop the FFS as due to its calculation nature it is capable of relating water depth variation in manholes to rainfall intensities. The campus of the University of Lille is used as an experimental site to test and evaluate the FFS proposed in this paper.
Lovring, Maite Monica; Löwe, Roland; Courdent, Vianney Augustin Thomas
An early flood warning system has been developed for urban catchments and is currently running in online operation in Copenhagen. The system is highly dependent on the quality of rainfall forecast inputs. An investigation of precipitation inputs from Radar Nowcast (RN), Numerical Weather Prediction...
Ravazzani, Giovanni; Amengual, Arnau; Ceppi, Alessandro; Homar, Víctor; Romero, Romu; Lombardi, Gabriele; Mancini, Marco
Analysis of ensemble forecasting strategies, which can provide a tangible backing for flood early warning procedures and mitigation measures over the Mediterranean region, is one of the fundamental motivations of the international HyMeX programme. Here, we examine two severe hydrometeorological episodes that affected the Milano urban area and for which the complex flood protection system of the city did not completely succeed. Indeed, flood damage have exponentially increased during the last 60 years, due to industrial and urban developments. Thus, the improvement of the Milano flood control system needs a synergism between structural and non-structural approaches. First, we examine how land-use changes due to urban development have altered the hydrological response to intense rainfalls. Second, we test a flood forecasting system which comprises the Flash-flood Event-based Spatially distributed rainfall-runoff Transformation, including Water Balance (FEST-WB) and the Weather Research and Forecasting (WRF) models. Accurate forecasts of deep moist convection and extreme precipitation are difficult to be predicted due to uncertainties arising from the numeric weather prediction (NWP) physical parameterizations and high sensitivity to misrepresentation of the atmospheric state; however, two hydrological ensemble prediction systems (HEPS) have been designed to explicitly cope with uncertainties in the initial and lateral boundary conditions (IC/LBCs) and physical parameterizations of the NWP model. No substantial differences in skill have been found between both ensemble strategies when considering an enhanced diversity of IC/LBCs for the perturbed initial conditions ensemble. Furthermore, no additional benefits have been found by considering more frequent LBCs in a mixed physics ensemble, as ensemble spread seems to be reduced. These findings could help to design the most appropriate ensemble strategies before these hydrometeorological extremes, given the computational
Abstract: The original urban surface structure changed a lot because of the rapid development of urbanization. Impermeable area has increased a lot. It causes great pressure for city flood control and drainage. Songmushan reservoir basin with high degree of urbanization is taken for an example. Pixel from Landsat is decomposed by Linear spectral mixture model and the proportion of urban area in it is considered as impervious rate. Based on impervious rate data before and after urbanization, an physically based distributed hydrological model, Liuxihe Model, is used to simulate the process of hydrology. The research shows that the performance of the flood forecasting of high urbanization area carried out with Liuxihe Model is perfect and can meet the requirement of the accuracy of city flood control and drainage. The increase of impervious area causes conflux speed more quickly and peak flow to be increased. It also makes the time of peak flow advance and the runoff coefficient increase. Key words: Liuxihe Model; Impervious rate; City flood control and drainage; Urbanization; Songmushan reservoir basin
Kobold, Mira; Suselj, Kay
The timely and accurate flood forecasting is essential for the reliable flood warning. The effectiveness of flood warning is dependent on the forecast accuracy of certain physical parameters, such as the peak magnitude of the flood, its timing, location and duration. The conceptual rainfall - runoff models enable the estimation of these parameters and lead to useful operational forecasts. The accurate rainfall is the most important input into hydrological models. The input for the rainfall can be real time rain-gauges data, or weather radar data, or meteorological forecasted precipitation. The torrential nature of streams and fast runoff are characteristic for the most of the Slovenian rivers. Extensive damage is caused almost every year- by rainstorms affecting different regions of Slovenia' The lag time between rainfall and runoff is very short for Slovenian territory and on-line data are used only for now casting. Forecasted precipitations are necessary for hydrological forecast for some days ahead. ECMWF (European Centre for Medium-Range Weather Forecasts) gives general forecast for several days ahead while more detailed precipitation data with limited area ALADIN/Sl model are available for two days ahead. There is a certain degree of uncertainty using such precipitation forecasts based on meteorological models. The variability of precipitation is very high in Slovenia and the uncertainty of ECMWF predicted precipitation is very large for Slovenian territory. ECMWF model can predict precipitation events correctly, but underestimates amount of precipitation in general The average underestimation is about 60% for Slovenian region. The predictions of limited area ALADIN/Si model up to; 48 hours ahead show greater applicability in hydrological forecasting. The hydrological models are sensitive to precipitation input. The deviation of runoff is much bigger than the rainfall deviation. Runoff to rainfall error fraction is about 1.6. If spatial and time distribution
Chen, Yangbo; Zhou, Haolan; Zhang, Hui; Du, Guoming; Zhou, Jinhui
In the past decades, China has observed rapid urbanization, the nation's urban population reached 50% in 2000, and is still in steady increase. Rapid urbanization in China has an adverse impact on urban hydrological processes, particularly in increasing the urban flood risks and causing serious urban flooding losses. Urban flooding also increases health risks such as causing epidemic disease break out, polluting drinking water and damaging the living environment. In the highly urbanized area, non-engineering measurement is the main way for managing urban flood risk, such as flood risk warning. There is no mature method and pilot study for urban flood risk warning, the purpose of this study is to propose the urban flood risk warning method for the rapidly urbanized Chinese cities. This paper first presented an urban flood forecasting model, which produces urban flood inundation index for urban flood risk warning. The model has 5 modules. The drainage system and grid dividing module divides the whole city terrain into drainage systems according to its first-order river system, and delineates the drainage system into grids based on the spatial structure with irregular gridding technique; the precipitation assimilation module assimilates precipitation for every grids which is used as the model input, which could either be the radar based precipitation estimation or interpolated one from rain gauges; runoff production module classifies the surface into pervious and impervious surface, and employs different methods to calculate the runoff respectively; surface runoff routing module routes the surface runoff and determines the inundation index. The routing on surface grid is calculated according to the two dimensional shallow water unsteady flow algorithm, the routing on land channel and special channel is calculated according to the one dimensional unsteady flow algorithm. This paper then proposed the urban flood risk warning method that is called DPSIR model based
Han, Shasha; Coulibaly, Paulin
Over the past few decades, floods have been seen as one of the most common and largely distributed natural disasters in the world. If floods could be accurately forecasted in advance, then their negative impacts could be greatly minimized. It is widely recognized that quantification and reduction of uncertainty associated with the hydrologic forecast is of great importance for flood estimation and rational decision making. Bayesian forecasting system (BFS) offers an ideal theoretic framework for uncertainty quantification that can be developed for probabilistic flood forecasting via any deterministic hydrologic model. It provides suitable theoretical structure, empirically validated models and reasonable analytic-numerical computation method, and can be developed into various Bayesian forecasting approaches. This paper presents a comprehensive review on Bayesian forecasting approaches applied in flood forecasting from 1999 till now. The review starts with an overview of fundamentals of BFS and recent advances in BFS, followed with BFS application in river stage forecasting and real-time flood forecasting, then move to a critical analysis by evaluating advantages and limitations of Bayesian forecasting methods and other predictive uncertainty assessment approaches in flood forecasting, and finally discusses the future research direction in Bayesian flood forecasting. Results show that the Bayesian flood forecasting approach is an effective and advanced way for flood estimation, it considers all sources of uncertainties and produces a predictive distribution of the river stage, river discharge or runoff, thus gives more accurate and reliable flood forecasts. Some emerging Bayesian forecasting methods (e.g. ensemble Bayesian forecasting system, Bayesian multi-model combination) were shown to overcome limitations of single model or fixed model weight and effectively reduce predictive uncertainty. In recent years, various Bayesian flood forecasting approaches have been
Moon, Y. I.; Kim, M. S.; Choi, J. H.; Yuk, G. M.
eavy rainfall has become a recent major cause of urban area flooding due to the climate change and urbanization. To prevent property damage along with casualties, a system which can alert and forecast urban flooding must be developed. Optimal performance of reducing flood damage can be expected of urban drainage facilities when operated in smaller rainfall events over extreme ones. Thus, the purpose of this study is to execute: A) flood forecasting system using runoff analysis based on short term rainfall; and B) flood warning system which operates based on the data from pump stations and rainwater storage in urban basins. In result of the analysis, it is shown that urban drainage facilities using short term rainfall forecasting data by radar will be more effective to reduce urban flood damage than using only the inflow data of the facility. Keywords: Heavy Rainfall, Urban Flood, Short-term Rainfall Forecasting, Optimal operating of urban drainage facilities. AcknowledgmentsThis research was supported by a grant (17AWMP-B066744-05) from Advanced Water Management Research Program (AWMP) funded by Ministry of Land, Infrastructure and Transport of Korean government.
Kasaee Roodsari, B.; Chandler, D. G.
A real-time flood forecast system is presented to provide emergency management authorities sufficient lead time to execute plans for evacuation and asset protection in urban watersheds. This study investigates the performance of two hybrid models for real-time flood forecasting at different subcatchments of Ley Creek watershed, a heavily urbanized watershed in the vicinity of Syracuse, New York. Hybrid models include Wavelet-Based Artificial Neural Network (WANN) and Wavelet-Based Adaptive Neuro-Fuzzy Inference System (WANFIS). Both models are developed on the basis of real time stream network sensing. The wavelet approach is applied to decompose the collected water depth timeseries to Approximation and Detail components. The Approximation component is then used as an input to ANN and ANFIS models to forecast water level at lead times of 1 to 10 hours. The performance of WANN and WANFIS models are compared to ANN and ANFIS models for different lead times. Initial results demonstrated greater predictive power of hybrid models.
Thorndahl, Søren Liedtke; Nielsen, Jesper Ellerbæk; Jensen, David Getreuer
Flooding produced by high-intensive local rainfall and drainage system capacity exceedance can have severe impacts in cities. In order to prepare cities for these types of flood events – especially in the future climate – it is valuable to be able to simulate these events numerically both...... historically and in real-time. There is a rather untested potential in real-time prediction of urban floods. In this paper radar data observations with different spatial and temporal resolution, radar nowcasts of 0–2 h lead time, and numerical weather models with lead times up to 24 h are used as inputs...... to an integrated flood and drainage systems model in order to investigate the relative difference between different inputs in predicting future floods. The system is tested on a small town Lystrup in Denmark, which has been flooded in 2012 and 2014. Results show it is possible to generate detailed flood maps...
Khamutova, M. V.; Rezchikov, A. F.; Kushnikov, V. A.; Ivaschenko, V. A.; Bogomolov, A. S.; Filimonyuk, L. Yu; Dolinina, O. N.; Kushnikova, E. V.; Shulga, T. E.; Tverdokhlebov, V. A.; Fominykh, D. S.
The article presents the development of a mathematical model of the system dynamics. Mathematical model allows forecasting the characteristics of flood effects. Model is based on a causal diagram and is presented by a system of nonlinear differential equations. Simulated characteristics are the nodes of the diagram, and edges define the functional relationships between them. The numerical solution of the system of equations using the Runge-Kutta method was obtained. Computer experiments to determine the characteristics on different time interval have been made and results of experiments have been compared with real data of real flood. The obtained results make it possible to assert that the developed model is valid. The results of study are useful in development of an information system for the operating and dispatching staff of the Ministry of the Russian Federation for Civil Defence, Emergencies and Elimination of Consequences of Natural Disasters (EMERCOM).
Revilla Romero, B.
Flooding is a natural global phenomenon but in many cases is exacerbated by human activity. Although flooding generally affects humans in a negative way, bringing death, suffering, and economic impacts, it also has potentially beneficial effects. Early flood warning and forecasting systems, as well
Emerton, Rebecca E.; Stephens, Elisabeth M.; Pappenberger, Florian; Pagano, Thomas P.; Weerts, A.H.; Wood, A.; Salamon, Peter; Brown, James D.; Hjerdt, Niclas; Donnelly, Chantal; Baugh, Calum A.; Cloke, Hannah L.
Floods are the most frequent of natural disasters, affecting millions of people across the globe every year. The anticipation and forecasting of floods at the global scale is crucial to preparing for severe events and providing early awareness where local flood models and warning services may not
Full Text Available TIGGE (THORPEX International Grand Global Ensemble was a major part of the THORPEX (Observing System Research and Predictability Experiment. It integrates ensemble precipitation products from all the major forecast centers in the world and provides systematic evaluation on the multimodel ensemble prediction system. Development of meteorologic-hydrologic coupled flood forecasting model and early warning model based on the TIGGE precipitation ensemble forecast can provide flood probability forecast, extend the lead time of the flood forecast, and gain more time for decision-makers to make the right decision. In this study, precipitation ensemble forecast products from ECMWF, NCEP, and CMA are used to drive distributed hydrologic model TOPX. We focus on Yi River catchment and aim to build a flood forecast and early warning system. The results show that the meteorologic-hydrologic coupled model can satisfactorily predict the flow-process of four flood events. The predicted occurrence time of peak discharges is close to the observations. However, the magnitude of the peak discharges is significantly different due to various performances of the ensemble prediction systems. The coupled forecasting model can accurately predict occurrence of the peak time and the corresponding risk probability of peak discharge based on the probability distribution of peak time and flood warning, which can provide users a strong theoretical foundation and valuable information as a promising new approach.
Full Text Available Recent large floods in Europe have led to increased interest in research and development of flood forecasting systems. Some of these events have been provoked by some of the wettest rainfall periods on record which has led to speculation that such extremes are attributable in some measure to anthropogenic global warming and represent the beginning of a period of higher flood frequency. Whilst current trends in extreme event statistics will be difficult to discern, conclusively, there has been a substantial increase in the frequency of high floods in the 20th century for basins greater than 2x105 km2. There is also increasing that anthropogenic forcing of climate change may lead to an increased probability of extreme precipitation and, hence, of flooding. There is, therefore, major emphasis on the improvement of operational flood forecasting systems in Europe, with significant European Community spending on research and development on prototype forecasting systems and flood risk management projects. This Special Issue synthesises the most relevant scientific and technological results presented at the International Conference on Flood Forecasting in Europe held in Rotterdam from 3-5 March 2003. During that meeting 150 scientists, forecasters and stakeholders from four continents assembled to present their work and current operational best practice and to discuss future directions of scientific and technological efforts in flood prediction and prevention. The papers presented at the conference fall into seven themes, as follows.
Full Text Available China is the nation with the fastest urbanization in the past decades which has caused serious urban flooding. Flood forecasting is regarded as one of the important flood mitigation methods, and is widely used in catchment flood mitigation, but is not widely used in urban flooding mitigation. This paper, employing the SWMM model, one of the widely used urban flood planning and management models, simulates the urban flooding of Dongguan City in the rapidly urbanized southern China. SWMM is first set up based on the DEM, digital map and underground pipeline network, then parameters are derived based on the properties of the subcatchment and the storm sewer conduits; the parameter sensitivity analysis shows the parameter robustness. The simulated results show that with the 1-year return period precipitation, the studied area will have no flooding, but for the 2-, 5-, 10- and 20-year return period precipitation, the studied area will be inundated. The results show the SWMM model is promising for urban flood forecasting, but as it has no surface runoff routing, the urban flooding could not be forecast precisely.
Baugh, Calum; Pappenberger, Florian; Wetterhall, Fredrik; Hewson, Tim; Zsoter, Ervin
The sudden and devastating nature of flash flood events means it is imperative to provide early warnings such as those derived from Numerical Weather Prediction (NWP) forecasts. Currently such systems exist on basin, national and continental scales in Europe, North America and Australia but rely on high resolution NWP forecasts or rainfall-radar nowcasting, neither of which have global coverage. To produce global flash flood forecasts this work investigates the possibility of using forecasts from a global NWP system. In particular we: (i) discuss how global NWP can be used for flash flood forecasting and discuss strengths and weaknesses; (ii) demonstrate how a robust evaluation can be performed given the rarity of the event; (iii) highlight the challenges and opportunities in communicating flash flood uncertainty to decision makers; and (iv) explore future developments which would significantly improve global flash flood forecasting. The proposed forecast system uses ensemble surface runoff forecasts from the ECMWF H-TESSEL land surface scheme. A flash flood index is generated using the ERIC (Enhanced Runoff Index based on Climatology) methodology [Raynaud et al., 2014]. This global methodology is applied to a series of flash floods across southern Europe. Results from the system are compared against warnings produced using the higher resolution COSMO-LEPS limited area model. The global system is evaluated by comparing forecasted warning locations against a flash flood database of media reports created in partnership with floodlist.com. To deal with the lack of objectivity in media reports we carefully assess the suitability of different skill scores and apply spatial uncertainty thresholds to the observations. To communicate the uncertainties of the flash flood system output we experiment with a dynamic region-growing algorithm. This automatically clusters regions of similar return period exceedence probabilities, thus presenting the at-risk areas at a spatial
Ali Awan, Shaukat
Meteorologically, there are two situations which may cause three types of floods in Indus Basin in Pakistan: i) Meteorological Situation for Category-I Floods when the seasonal low is a semi permanent weather system situated over south eastern Balochistan, south western Punjab, adjoining parts of Sindh get intensified and causes the moisture from the Arabian Sea to be brought up to upper catchments of Chenab and Jhelum rivers. (ii) Meteorological Situation for Category-11 and Category-111 Floods, which is linked with monsoon low/depression. Such monsoon systems originate in Bay of Bengal region and then move across India in general west/north westerly direction arrive over Rajasthan or any of adjoining states of India. Flood management in Pakistan is multi-functional process involving a number of different organizations. The first step in the process is issuance of flood forecast/warning, which is performed by Pakistan Meteorological Department (PMD) utilizing satellite cloud pictures and quantitative precipitation measurement radar data, in addition to the conventional weather forecasting facilities. For quantitative flood forecasting, hydrological data is obtained through the Provincial Irrigation Department and WAPDA. Furthermore, improved rainfall/runoff and flood routing models have been developed to provide more reliable and explicit flood information to a flood prone population.(Author)
Ullah, Nazrin; Choudhury, P.
The aim of the present study is to investigate applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood flow in a river system. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. The technique is applied to forecast discharge at a downstream station using flow information at various upstream stations. A total of three years data has been selected for the implementation of this model. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate efficiency of the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and Coefficient of Efficiency (CE) are used to evaluate performance of the ANFIS models in forecasting river flood. The values of the indices show that ANFIS model can accurately and reliably be used to forecast flood in a river system.
de Bruyn Bertrand
Full Text Available Providing forecasts for flow rates and water levels during floods have to be associated with uncertainty estimates. The forecast sources of uncertainty are plural. For hydrological forecasts (rainfall-runoff performed using a deterministic hydrological model with basic physics, two main sources can be identified. The first obvious source is the forcing data: rainfall forecast data are supplied in real time by meteorological forecasting services to the Flood Forecasting Service within a range between a lowest and a highest predicted discharge. These two values define an uncertainty interval for the rainfall variable provided on a given watershed. The second source of uncertainty is related to the complexity of the modeled system (the catchment impacted by the hydro-meteorological phenomenon, the number of variables that may describe the problem and their spatial and time variability. The model simplifies the system by reducing the number of variables to a few parameters. Thus it contains an intrinsic uncertainty. This model uncertainty is assessed by comparing simulated and observed rates for a large number of hydro-meteorological events. We propose a method based on fuzzy arithmetic to estimate the possible range of flow rates (and levels of water making a forecast based on possible rainfalls provided by forcing and uncertainty model. The model uncertainty is here expressed as a range of possible values. Both rainfall and model uncertainties are combined with fuzzy arithmetic. This method allows to evaluate the prediction uncertainty range. The Flood Forecasting Service of Oise and Aisne rivers, in particular, monitors the upstream watershed of the Oise at Hirson. This watershed’s area is 310 km2. Its response time is about 10 hours. Several hydrological models are calibrated for flood forecasting in this watershed and use the rainfall forecast. This method presents the advantage to be easily implemented. Moreover, it permits to be carried out
Rakovec, Oldrich; Weerts, Albrecht; Uijlenhoet, Remko; Hazenberg, Pieter; Torfs, Paul
Accurate flood forecasts have been a challenging topic in hydrology for decades. Uncertainty in hydrological forecasts is due to errors in initial state (e.g. forcing errors in historical mode), errors in model structure and parameters and last but not least the errors in model forcings (weather forecasts) during the forecast mode. More accurate flood forecasts can be obtained through data assimilation by merging observations with model simulations. This enables to identify the sources of uncertainties in the flood forecasting system. Our aim is to assess the different sources of error that affect the initial state and to investigate how they propagate through hydrological models with different levels of spatial variation, starting from lumped models. The knowledge thus obtained can then be used in a data assimilation scheme to improve the flood forecasts. This study presents the first results of this framework and focuses on quantifying precipitation errors and its effect on discharge simulations within the Ourthe catchment (1600 km2), which is situated in the Belgian Ardennes and is one of the larger subbasins of the Meuse River. Inside the catchment, hourly rain gauge information from 10 different locations is available over a period of 15 years. Based on these time series, the bootstrap method has been applied to generate precipitation ensembles. These were then used to simulate the catchment's discharges at the outlet. The corresponding streamflow ensembles were further assimilated with observed river discharges to update the model states of lumped hydrological models (R-PDM, HBV) through Residual Resampling. This particle filtering technique is a sequential data assimilation method and takes no prior assumption of the probability density function for the model states, which in contrast to the Ensemble Kalman filter does not have to be Gaussian. Our further research will be aimed at quantifying and reducing the sources of uncertainty that affect the initial
In order to issue an accurate warning for flood, a better or appropriate quantitative forecasting of precipitationis required. In view of this, the present study intends to validate the quantitative precipitationforecast (QPF) issued during southwest monsoon season for six river catchments (basin) under theflood meteorological ...
Lhomme, Serge; Serre, Damien; Diab, Youssef; Laganier, Richard
In Europe, river floods have been increasing in frequency and severity [Szöllösi-Nagy and Zevenbergen, 2005]. Moreover, climate change is expected to exacerbate the frequency and intensity of hydro meteorological disaster [IPCC, 2007]. Despite efforts made to maintain the flood defense assets, we often observe levee failures leading to finally increase flood risk in protected area. Furthermore, flood forecasting models, although benefiting continuous improvements, remain partly inaccurate due to uncertainties arising all along data calculation processes. In the same time, the year 2007 marks a turning point in history: half of the world population now lives in cities (UN-Habitat, 2007). Moreover, the total urban population is expected to double from two to four billion over the next 30 to 35 years (United Nations, 2006). This growing rate is equivalent to the creation of a new city of one million inhabitants every week, and this during the next four decades [Flood resilience Group]. So, this quick urban development coupled with technical failures and climate change have increased flood risk and corresponding challenges to urban flood risk management [Ashley et al., 2007], [Nie et al., 2009]. These circumstances oblige to manage flood risk by integrating new concepts like urban resilience. In recent years, resilience has become a central concept for risk management. This concept has emerged because a more resilient system is less vulnerable to risk and, therefore, more sustainable [Serre et al., 2010]. But urban flood resilience is a concept that has not yet been directly assessed. Therefore, when decision makers decide to use the resilience concept to manage urban flood, they have no tool to help them. That is why this paper proposes a methodology to assess urban flood resilience in order to make this concept operational. Networks affect the well-being of the people and the smooth functioning of services and, more generally, of economical activities. Yet
Sumi, S. J.; Ferreira, C.
Extreme flood events are the costliest natural hazards impacting the US and frequently cause extensive damages to infrastructure, disruption to economy and loss of lives. In 2016, Hurricane Matthew brought severe damage to South Carolina and demonstrated the importance of accurate flood hazard predictions that requires the integration of riverine and coastal model forecasts for total water prediction in coastal and tidal areas. The National Weather Service (NWS) and the National Ocean Service (NOS) provide flood forecasts for almost the entire US, still there are service-gap areas in tidal regions where no official flood forecast is available. The National capital region is vulnerable to multi-flood hazards including high flows from annual inland precipitation events and surge driven coastal inundation along the tidal Potomac River. Predicting flood levels on such tidal areas in river-estuarine zone is extremely challenging. The main objective of this study is to develop the next generation of flood forecast systems capable of providing accurate and timely information to support emergency management and response in areas impacted by multi-flood hazards. This forecast system is capable of simulating flood levels in the Potomac and Anacostia River incorporating the effects of riverine flooding from the upstream basins, urban storm water and tidal oscillations from the Chesapeake Bay. Flood forecast models developed so far have been using riverine data to simulate water levels for Potomac River. Therefore, the idea is to use forecasted storm surge data from a coastal model as boundary condition of this system. Final output of this validated model will capture the water behavior in river-estuary transition zone far better than the one with riverine data only. The challenge for this iFLOOD forecast system is to understand the complex dynamics of multi-flood hazards caused by storm surges, riverine flow, tidal oscillation and urban storm water. Automated system
While agglomerations along the Rhine are confronted with the uncertainties of an increasing flood risk due to climate change, different programs are claiming urban river front sites. Simultaneously, urban development, flood management, as well as navigation and environmental protection are
volume, increasing capacity of drainage systems, spatial planning, building regulations, etc. Resilience also considers resilience of population to floods and it's measured with time. Assessment of resilience that is focused on population is following bottom-up approach starting from individual and then assessing community level. Building resilience involves also contribution of social networks, increasing response capacity of communities, self-organization, learning and education and cheering adaptation culture. Measures for improving social side of resilience covers: raising public awareness, implementation of flood forecasting and warning, emergency response planning and training, sharing information, education and communication. Most of these aspects are analyzed with the CORFU FP7 project. Collaborative Research on Flood Resilience in Urban areas (CORFU) is a major project involving 17 European and Asian institutions, funded by a grant from the European Commission under the Seventh Framework Programme. The overall aim of CORFU is to enable European and Asian partners to learn from each other through joint investigation, development, implementation and dissemination of short to medium term strategies that will enable more scientifically sound management of the consequences of urban flooding in the future and to develop resilience strategies according to each situation. The CORFU project looks at advanced and novel strategies and provide adequate measures for improved flood management in cities. The differences in urban flooding problems in Asia and in Europe range from levels of economic development, infrastructure age, social systems and decision making processes, to prevailing drainage methods, seasonality of rainfall patterns and climate change trends. The study cases are, in Europe, the cities of Hamburg, Barcelona and Nice, and in Asia, Beijing, Dhaka, Mumbai, Taipei, Seoul and Incheon.
Nguyen, V.T.; Eberl, H.
Every year, flooding does not only cause property damage of billions of dollars, but also threats to millions of human life around the world. The ability to accurately predict the extreme flooding in urban areas is of obvious importance in order to reduce flooding risks and to improve public safety. In this paper, a complete computational tool is presented that includes pre-processing, meshing, calculating and post-processing modules. The pre-processing procedure is used to interpolate the geometry of the river and floodplains where the data can not be obtained directly from measurements. The meshing procedure is implemented by a triangle mesh generator. The computational procedure is based on a Finite Element Method to discretize the two-dimensional depth-averaged equations for shallow water flow. The post-processing procedure, finally, is interfaced with Geographic Information Systems (GIS), which can serve as a tool for monitoring and as an early warning system. The numerical model is verified and calibrated through many practical projects of flood protection for rivers in Germany. The numerical results show a very good agreement with data from the field survey, as well as data from past flood events. Thus the numerical model can be used as an important tool for flood prediction. (author)
Alfieri, L.; Dottori, F.; Kalas, M.; Lorini, V.; Bianchi, A.; Hirpa, F. A.; Feyen, L.; Salamon, P.
Global flood forecasting and monitoring systems are nowadays a reality and are being applied by an increasing range of users and practitioners in disaster risk management. Furthermore, there is an increasing demand from users to integrate flood early warning systems with risk based forecasts, combining streamflow estimations with expected inundated areas and flood impacts. To this end, we have developed an experimental procedure for near-real time flood mapping and impact assessment based on the daily forecasts issued by the Global Flood Awareness System (GloFAS). The methodology translates GloFAS streamflow forecasts into event-based flood hazard maps based on the predicted flow magnitude and the forecast lead time and a database of flood hazard maps with global coverage. Flood hazard maps are then combined with exposure and vulnerability information to derive flood risk. Impacts of the forecasted flood events are evaluated in terms of flood prone areas, potential economic damage, and affected population, infrastructures and cities. To further increase the reliability of the proposed methodology we integrated model-based estimations with an innovative methodology for social media monitoring, which allows for real-time verification of impact forecasts. The preliminary tests provided good results and showed the potential of the developed real-time operational procedure in helping emergency response and management. In particular, the link with social media is crucial for improving the accuracy of impact predictions.
Kalas, Milan; Kliment, Tomas; Salamon, Peter
Social and mainstream media monitoring is being more and more recognized as valuable source of information in disaster management and response. The information on ongoing disasters could be detected in very short time and the social media can bring additional information to traditional data feeds (ground, remote observation schemes). Probably the biggest attempt to use the social media in the crisis management was the activation of the Digital Humanitarian Network by the United Nations Office for the Coordination of Humanitarian Affairs in response to Typhoon Yolanda. The network of volunteers performing rapid needs & damage assessment by tagging reports posted to social media which were then used by machine learning classifiers as a training set to automatically identify tweets referring to both urgent needs and offers of help. In this work we will present the potential of coupling a social media streaming and news monitoring application ( GlobalFloodNews - www.globalfloodsystem.com) with a flood forecasting system (www.globalfloods.eu) and the geo-catalogue of the OGC services discovered in the Google Search Engine (WMS, WFS, WCS, etc.) to provide a full suite of information available to crisis management centers as fast as possible. In GlobalFloodNews we use advanced filtering of the real-time Twitter stream, where the relevant information is automatically extracted using natural language and signal processing techniques. The keyword filters are adjusted and optimized automatically using machine learning algorithms as new reports are added to the system. In order to refine the search results the forecasting system will be triggering an event-based search on the social media and OGC services relevant for crisis response (population distribution, critical infrastructure, hospitals etc.). The current version of the system makes use of USHAHIDI Crowdmap platform, which is designed to easily crowdsource information using multiple channels, including SMS, email
Stuparu, Dana; Bachmann, Daniel; Bogaard, Tom; Twigt, Daniel; Verkade, Jan; de Bruijn, Karin; de Leeuw, Annemargreet
Flood forecasts, warning and emergency response are important components in flood risk management. Most flood forecasting systems use models to translate weather predictions to forecasted discharges or water levels. However, this information is often not sufficient for real time decisions. A sound understanding of the reliability of embankments and flood dynamics is needed to react timely and reduce the negative effects of the flood. Where are the weak points in the dike system? When, how much and where the water will flow? When and where is the greatest impact expected? Model-based flood impact forecasting tries to answer these questions by adding new dimensions to the existing forecasting systems by providing forecasted information about: (a) the dike strength during the event (reliability), (b) the flood extent in case of an overflow or a dike failure (flood spread) and (c) the assets at risk (impacts). This work presents three study-cases in which such a set-up is applied. Special features are highlighted. Forecasting of dike strength. The first study-case focusses on the forecast of dike strength in the Netherlands for the river Rhine branches Waal, Nederrijn and IJssel. A so-called reliability transformation is used to translate the predicted water levels at selected dike sections into failure probabilities during a flood event. The reliability of a dike section is defined by fragility curves - a summary of the dike strength conditional to the water level. The reliability information enhances the emergency management and inspections of embankments. Ensemble forecasting. The second study-case shows the setup of a flood impact forecasting system in Dumfries, Scotland. The existing forecasting system is extended with a 2D flood spreading model in combination with the Delft-FIAT impact model. Ensemble forecasts are used to make use of the uncertainty in the precipitation forecasts, which is useful to quantify the certainty of a forecasted flood event. From global
Eduardo Dourado Argolo
Full Text Available The study area is located along the Rio das Antas basin in the city of Anápolis, Goiás. This study exemplifies an urban area exposed to flooding by rainwater. Decline in the permeability of the river basin area is result of significant real state development in recent years. This study proposes to simulate water flows and respective flooding areas along different sections of the River in response to different rainfall intensities. The simulated flow rates are the result of interpretation of land use scenarios and hydrologic modeling of the river basin area. The rational method and the Bernoulli equation were used in the hydraulic simulation model of the computer program HEC-RAS (Hydrologic Engineering Center's River Analysis System...
Pillosu, F. M.; Jurlina, T.; Baugh, C.; Tsonevsky, I.; Hewson, T.; Prates, F.; Pappenberger, F.; Prudhomme, C.
During hurricane Harvey the greater east Texas area was affected by extensive flash flooding. Their localised nature meant they were too small for conventional large scale flood forecasting systems to capture. We are testing the use of two real time forecast products from the European Centre for Medium-range Weather Forecasts (ECMWF) in combination with local vulnerability information to provide flash flood forecasting tools at the medium range (up to 7 days ahead). Meteorological forecasts are the total precipitation extreme forecast index (EFI), a measure of how the ensemble forecast probability distribution differs from the model-climate distribution for the chosen location, time of year and forecast lead time; and the shift of tails (SOT) which complements the EFI by quantifying how extreme an event could potentially be. Both products give the likelihood of flash flood generating precipitation. For hurricane Harvey, 3-day EFI and SOT products for the period 26th - 29th August 2017 were used, generated from the twice daily, 18 km, 51 ensemble member ECMWF Integrated Forecast System. After regridding to 1 km resolution the forecasts were combined with vulnerable area data to produce a flash flood hazard risk area. The vulnerability data were floodplains (EU Joint Research Centre), road networks (Texas Department of Transport) and urban areas (Census Bureau geographic database), together reflecting the susceptibility to flash floods from the landscape. The flash flood hazard risk area forecasts were verified using a traditional approach against observed National Weather Service flash flood reports, a total of 153 reported flash floods have been detected in that period. Forecasts performed best for SOT = 5 (hit ratio = 65%, false alarm ratio = 44%) and EFI = 0.7 (hit ratio = 74%, false alarm ratio = 45%) at 72 h lead time. By including the vulnerable areas data, our verification results improved by 5-15%, demonstrating the value of vulnerability information within
Pedersen, Jonas Wied; Courdent, Vianney Augustin Thomas; Vezzaro, Luca
This research shows how ensemble weather forecasts can be used to generate urban runoff forecasts up to 53 hours into the future. The results highlight systematic differences between ensemble members that needs to be accounted for when these forecasts are used in practice.......This research shows how ensemble weather forecasts can be used to generate urban runoff forecasts up to 53 hours into the future. The results highlight systematic differences between ensemble members that needs to be accounted for when these forecasts are used in practice....
Coughlan, E.R.; Stephens, E.; Bischiniotis, K.; van Aalst, M.; van den Hurk, B.J.J.M.; Mason, S.; Nissan, H.; Pappenberger, F.
In light of strong encouragement for disaster managers to use climate services for flood preparation, we question whether seasonal rainfall forecasts should indeed be used as indicators of the likelihood of flooding. Here, we investigate the primary indicators of flooding at the seasonal timescale
Full Text Available Lepenica river basin territory has became axis of economic and urban development of Šumadija district. However, considering Lepenica River with its tributaries, and their disordered river regime, there is insufficient of water for water supply and irrigation, while on the other hand, this area is suffering big flood and torrent damages (especially Kragujevac basin. The paper presents flood problems in the river basin, maximum water level forecasts, and flood control measures carried out until now. Some of the potential solutions, aiming to achieve the effective flood control, are suggested as well.
The floods of January 1969 in south-coastal California provide a timely example of the effect of urban sprawl on flood damage. Despite recordbreaking, or near recordbreaking, stream discharges, damage was minimal in the older developed areas that are protected against inundation and debris damage by carefully planned flood-control facilities, including debris basins and flood-conveyance channels. By contrast, heavy damage occurred in areas of more recent urban sprawl, where the hazards of inundation and debris or landslide damage have not been taken into consideration, and where the improvement and development of drainage or flood-control facilities have not kept pace with expanding urbanization.
Borga, M.; Anagnostou, E.N.; Bloeschl, G.; Creutin, J.-D.
Highlights: → We characterize flash flood events in various regions of Europe. → We provide guidance to improve observations and monitoring of flash floods. → Flash floods are associated to orography and are influenced by initial soil moisture conditions. → Models for flash flood forecasting and flash flood hazard assessment are illustrated and discussed. → We examine implications for flood risk policy and discuss recommendations received from end users. - Abstract: The management of flash flood hazards and risks is a critical component of public safety and quality of life. Flash-floods develop at space and time scales that conventional observation systems are not able to monitor for rainfall and river discharge. Consequently, the atmospheric and hydrological generating mechanisms of flash-floods are poorly understood, leading to highly uncertain forecasts of these events. The objective of the HYDRATE project has been to improve the scientific basis of flash flood forecasting by advancing and harmonising a European-wide innovative flash flood observation strategy and developing a coherent set of technologies and tools for effective early warning systems. To this end, the project included actions on the organization of the existing flash flood data patrimony across Europe. The final aim of HYDRATE was to enhance the capability of flash flood forecasting in ungauged basins by exploiting the extended availability of flash flood data and the improved process understanding. This paper provides a review of the work conducted in HYDRATE with a special emphasis on how this body of research can contribute to guide the policy-life cycle concerning flash flood risk management.
Fakhruddin, S. H. M.
Early warning is a key element for disaster risk reduction. In recent decades, major advancements have been made in medium range and seasonal flood forecasting. This progress provides a great opportunity to reduce agriculture damage and improve advisories for early action and planning for flood hazards. This approach can facilitate proactive rather than reactive management of the adverse consequences of floods. In the agricultural sector, for instance, farmers can take a diversity of options such as changing cropping patterns, applying fertilizer, irrigating and changing planting timing. An experimental medium range (1-10 day) flood forecasting model has been developed for Bangladesh and Thailand. It provides 51 sets of discharge ensemble forecasts of 1-10 days with significant persistence and high certainty. This type of forecast could assist farmers and other stakeholders for differential preparedness activities. These ensembles probabilistic flood forecasts have been customized based on user-needs for community-level application focused on agriculture system. The vulnerabilities of agriculture system were calculated based on exposure, sensitivity and adaptive capacity. Indicators for risk and vulnerability assessment were conducted through community consultations. The forecast lead time requirement, user-needs, impacts and management options for crops were identified through focus group discussions, informal interviews and community surveys. This paper illustrates potential applications of such ensembles for probabilistic medium range flood forecasts in a way that is not commonly practiced globally today.
Winston T. L. Chow; Brendan D. Cheong; Beatrice H. Ho
We investigated flooding patterns in the urbanised city-state of Singapore through a multimethod approach combining station precipitation data with archival newspaper and governmental records; changes in flash floods frequencies or reported impacts of floods towards Singapore society were documented. We subsequently discussed potential flooding impacts in the context of urban vulnerability, based on future urbanisation and forecasted precipitation projections for Singapore. We find that, desp...
Sörensen, Johanna; Persson, Andreas; Sternudd, Catharina
-term flood risk and harm the riverine ecosystems in urban as well as rural areas. In the present paper, we depart from resilience theory and suggest a concept to improve urban flood resilience. We identify areas where contemporary challenges call for improved collaborative urban flood management. The concept...... emphasizes resiliency and achieved synergy between increased capacity to handle stormwater runoff and improved experiential and functional quality of the urban environments. We identify research needs as well as experiments for improved sustainable and resilient stormwater management namely, flexibility...
Coughlan de Perez, Erin; van den Hurk, Bart; van Aalst, Maarten K.; Amuron, Irene; Bamanya, Deus; Hauser, Tristan; Jongma, Brenden; Lopez, Ana; Mason, Simon; Mendler de Suarez, Janot; Pappenberger, Florian; Rueth, Alexandra; Stephens, Elisabeth; Suarez, Pablo; Wagemaker, Jurjen; Zsoter, Ervin
Too often, credible scientific early warning information of increased disaster risk does not result in humanitarian action. With financial resources tilted heavily towards response after a disaster, disaster managers have limited incentive and ability to process complex scientific data, including uncertainties. These incentives are beginning to change, with the advent of several new forecast-based financing systems that provide funding based on a forecast of an extreme event. Given the changing landscape, here we demonstrate a method to select and use appropriate forecasts for specific humanitarian disaster prevention actions, even in a data-scarce location. This action-based forecasting methodology takes into account the parameters of each action, such as action lifetime, when verifying a forecast. Forecasts are linked with action based on an understanding of (1) the magnitude of previous flooding events and (2) the willingness to act "in vain" for specific actions. This is applied in the context of the Uganda Red Cross Society forecast-based financing pilot project, with forecasts from the Global Flood Awareness System (GloFAS). Using this method, we define the "danger level" of flooding, and we select the probabilistic forecast triggers that are appropriate for specific actions. Results from this methodology can be applied globally across hazards and fed into a financing system that ensures that automatic, pre-funded early action will be triggered by forecasts.
Ntegeka, Victor; Murla, Damian; Wang, Lipen; Foresti, Loris; Reyniers, Maarten; Delobbe, Laurent; Van Herk, Kristine; Van Ootegem, Luc; Willems, Patrick
Pluvial flood nowcasting is gaining ground not least because of the advancements in rainfall forecasting schemes. Short-term forecasts and applications have benefited from the availability of such forecasts with high resolution in space (~1km) and time (~5min). In this regard, it is vital to evaluate the potential of nowcasting products for urban inundation applications. One of the most advanced Quantitative Precipitation Forecasting (QPF) techniques is the Short-Term Ensemble Prediction System, which was originally co-developed by the UK Met Office and Australian Bureau of Meteorology. The scheme was further tuned to better estimate extreme and moderate events for the Belgian area (STEPS-BE). Against this backdrop, a probabilistic framework has been developed that consists of: (1) rainfall nowcasts; (2) sewer hydraulic model; (3) flood damage estimation; and (4) urban inundation risk mapping. STEPS-BE forecasts are provided at high resolution (1km/5min) with 20 ensemble members with a lead time of up to 2 hours using a 4 C-band radar composite as input. Forecasts' verification was performed over the cities of Leuven and Ghent and biases were found to be small. The hydraulic model consists of the 1D sewer network and an innovative 'nested' 2D surface model to model 2D urban surface inundations at high resolution. The surface components are categorized into three groups and each group is modelled using triangular meshes at different resolutions; these include streets (3.75 - 15 m2), high flood hazard areas (12.5 - 50 m2) and low flood hazard areas (75 - 300 m2). Functions describing urban flood damage and social consequences were empirically derived based on questionnaires to people in the region that were recently affected by sewer floods. Probabilistic urban flood risk maps were prepared based on spatial interpolation techniques of flood inundation. The method has been implemented and tested for the villages Oostakker and Sint-Amandsberg, which are part of the
Khalid, A.; Ferreira, C.
Coastal flooding is becoming a major threat to increased population in the coastal areas. To protect coastal communities from tropical storms & hurricane damages, early warning systems are being developed. These systems have the capability of real time flood forecasting to identify hazardous coastal areas and aid coastal communities in rescue operations. State of the art hydrodynamic models forced by atmospheric forcing have given modelers the ability to forecast storm surge, water levels and currents. This helps to identify the areas threatened by intense storms. Study on Chesapeake Bay area has gained national importance because of its combined riverine and coastal phenomenon, which leads to greater uncertainty in flood predictions. This study presents an automated flood forecast system developed by following Advanced Circulation (ADCIRC) Surge Guidance System (ASGS) guidelines and tailored to take in riverine and coastal boundary forcing, thus includes all the hydrodynamic processes to forecast total water in the Potomac River. As studies on tidal and riverine flow interaction are very scarce in number, our forecast system would be a scientific tool to examine such area and fill the gaps with precise prediction for Potomac River. Real-time observations from National Oceanic and Atmospheric Administration (NOAA) and field measurements have been used as model boundary feeding. The model performance has been validated by using major historical riverine and coastal flooding events. Hydrodynamic model ADCIRC produced promising predictions for flood inundation areas. As better forecasts can be achieved by using coupled models, this system is developed to take boundary conditions from Global WaveWatchIII for the research purposes. Wave and swell propagation will be fed through Global WavewatchIII model to take into account the effects of swells and currents. This automated forecast system is currently undergoing rigorous testing to include any missing parameters which
Dottori, Francesco; Kalas, Milan; Lorini, Valerio; Wania, Annett; Pappenberger, Florian; Salamon, Peter; Ramos, Maria Helena; Cloke, Hannah; Castillo, Carlos
Floods have devastating effects on lives and livelihoods around the world. Structural flood defence measures such as dikes and dams can help protect people. However, it is the emerging science and technologies for flood disaster management and preparedness, such as increasingly accurate flood forecasting systems, high-resolution satellite monitoring, rapid risk mapping, and the unique strength of social media information and crowdsourcing, that are most promising for reducing the impacts of flooding. Here, we describe an innovative framework which integrates in real-time two components of the Copernicus Emergency mapping services, namely the European Flood Awareness System and the satellite-based Rapid Mapping, with new procedures for rapid risk assessment and social media and news monitoring. The integrated framework enables improved flood impact forecast, thanks to the real-time integration of forecasting and monitoring components, and increases the timeliness and efficiency of satellite mapping, with the aim of capturing flood peaks and following the evolution of flooding processes. Thanks to the proposed framework, emergency responders will have access to a broad range of timely and accurate information for more effective and robust planning, decision-making, and resource allocation.
Li, Y.; Grimaldi, S.; Pauwels, V. R. N.; Walker, J. P.; Wright, A. J.
Flooding is one of the most destructive natural disasters, resulting in many deaths and billions of dollars of damages each year. An indispensable tool to mitigate the effect of floods is to provide accurate and timely forecasts. An operational flood forecasting system typically consists of a hydrologic model, converting rainfall data into flood volumes entering the river system, and a hydraulic model, converting these flood volumes into water levels and flood extents. Such a system is prone to various sources of uncertainties from the initial conditions, meteorological forcing, topographic data, model parameters and model structure. To reduce those uncertainties, current forecasting systems are typically calibrated and/or updated using ground-based streamflow measurements, and such applications are limited to well-gauged areas. The recent increasing availability of spatially distributed remote sensing (RS) data offers new opportunities to improve flood forecasting skill. Based on an Australian case study, this presentation will discuss the use of 1) RS soil moisture to constrain a hydrologic model, and 2) RS flood extent and level to constrain a hydraulic model.The GRKAL hydrological model is calibrated through a joint calibration scheme using both ground-based streamflow and RS soil moisture observations. A lag-aware data assimilation approach is tested through a set of synthetic experiments to integrate RS soil moisture to constrain the streamflow forecasting in real-time.The hydraulic model is LISFLOOD-FP which solves the 2-dimensional inertial approximation of the Shallow Water Equations. Gauged water level time series and RS-derived flood extent and levels are used to apply a multi-objective calibration protocol. The effectiveness with which each data source or combination of data sources constrained the parameter space will be discussed.
Full Text Available This paper reports on experience with developing the flood forecasting model for the Upper Danube basin and its operational use since 2006. The model system consists of hydrological and hydrodynamic components, and involves precipitation forecasts. The model parameters were estimated based on the dominant processes concept. Runoff data are assimilated in real time to update modelled soil moisture. An analysis of the model performance indicates 88% of the snow cover in the basin to be modelled correctly on more than 80% of the days. Runoff forecasting errors decrease with catchment area and increase with forecast lead time. The forecast ensemble spread is shown to be a meaningful indicator of the forecast uncertainty. During the 2013 flood, there was a tendency for the precipitation forecasts to underestimate event precipitation and for the runoff model to overestimate runoff generation which resulted in, overall, rather accurate runoff forecasts. It is suggested that the human forecaster plays an essential role in interpreting the model results and, if needed, adjusting them before issuing the forecasts to the general public.
Verkade, J.S.; Werner, M.G.F.
Flood risk can be reduced by means of flood forecasting, warning and response systems (FFWRS). These systems include a forecasting sub-system which is imperfect, meaning that inherent uncertainties in hydrological forecasts may result in false alarms and missed floods, or surprises. This forecasting
Full Text Available A multidisciplinary and integrated approach to the flood mitigation decision making process should provide the best response of society in a flood hazard situation including preparation works and post hazard mitigation. In Slovenia, there is a great lack of data on social aspects and public response to flood mitigation measures and information management. In this paper, two studies of flood perception in the Slovenian town Celje are represented. During its history, Celje was often exposed to floods, the most recent serious floods being in 1990 and in 1998, with a hundred and fifty return period and more than ten year return period, respectively. Two surveys were conducted in 1997 and 2003, with 157 participants from different areas of the town in the first, and 208 in the second study, aiming at finding the general attitude toward the floods. The surveys revealed that floods present a serious threat in the eyes of the inhabitants, and that the perception of threat depends, to a certain degree, on the place of residence. The surveys also highlighted, among the other measures, solidarity and the importance of insurance against floods.
Thorndahl, Søren; Nielsen, Jesper Ellerbæk; Jensen, David Getreuer
Flooding produced by high-intensive local rainfall and drainage system capacity exceedance can have severe impacts in cities. In order to prepare cities for these types of flood events – especially in the future climate – it is valuable to be able to simulate these events numericallyboth...... historically and in real-time. There is a rather untested potential in real-time prediction of urban floods. In this paper radar data observations with different spatial and temporal resolution, radar nowcasts of 0-2 hours leadtime, and numerical weather models with leadtimes up to 24 h are used as inputs...... to an integrated flood and drainage systems model with the purpose to investigate the potential for predicting future floods. The system is tested on a small town Lystrup in Denmark, which has been recently flooded. Results show that it is possible to generate detailed flood maps in real-time with high resolution...
E. Coughlan de Perez
Full Text Available In light of strong encouragement for disaster managers to use climate services for flood preparation, we question whether seasonal rainfall forecasts should indeed be used as indicators of the likelihood of flooding. Here, we investigate the primary indicators of flooding at the seasonal timescale across sub-Saharan Africa. Given the sparsity of hydrological observations, we input bias-corrected reanalysis rainfall into the Global Flood Awareness System to identify seasonal indicators of floodiness. Results demonstrate that in some regions of western, central, and eastern Africa with typically wet climates, even a perfect tercile forecast of seasonal total rainfall would provide little to no indication of the seasonal likelihood of flooding. The number of extreme events within a season shows the highest correlations with floodiness consistently across regions. Otherwise, results vary across climate regimes: floodiness in arid regions in southern and eastern Africa shows the strongest correlations with seasonal average soil moisture and seasonal total rainfall. Floodiness in wetter climates of western and central Africa and Madagascar shows the strongest relationship with measures of the intensity of seasonal rainfall. Measures of rainfall patterns, such as the length of dry spells, are least related to seasonal floodiness across the continent. Ultimately, identifying the drivers of seasonal flooding can be used to improve forecast information for flood preparedness and to avoid misleading decision-makers.
Full Text Available Flood forecasting is necessary to save lives and reduce damages. Reducing damages is important to save livelihoods and to reduce the recovery time. Flood alerts should contain expected time of the event, location and extent of the event. A flood alert is not only one message but part of a rehearsed flow of information using multiple canals. First people have to accept the fact that there might be a threat and what the threat is about. People need a reference to understand the situation and be aware of possible measures they can take to assure their own safety and reduce damages. Information to the general public has to be consistent with the information used by emergency services and has to be very clear about consequences and context of possible measures (as shelter in place or preventive evacuation. Emergency services should monitor how the public is responding to adapt their communication en operation during a crisis. Flood warnings and emergency services are often coordinated by different government organisations. This is an extra handicap for having consistent information out on time for people to use. In an information based society, where everyone has twitter, email and a camera, public organisations may have to trust the public more and send out the correct information as it comes in. In the Netherlands Rijkswaterstaat, the National Water Authority and the National Public Works Department, is responsible for or involved in forecasting in case of floods, policy studies on flood risk, policy studies on maintenance, assessment and design of flood defences, elaborating rules and regulations for flood defences, advice on crisis management to the national government and for maintaining the main infrastructure in the Netherlands (high ways and water ways. The Water Management Center in the Netherlands (WMCN has developed a number of models to provide flood forecasts. WMCN is run for and by all managers of flood defences and is hosted by
Hafiz, I; Sidek, L M; Basri, H; Fukami, K; Hanapi, M N; Livia, L; Nor, M D
Floods can bring such disasters to the affected dweller due to loss of properties, crops and even deaths. The damages to properties and crops by the severe flooding are occurred due to the increase in the economic value of the properties as well as the extent of the flood. Flood forecasting and warning system is one of the examples of the non-structural measures which can give early warning to the affected people. People who live near the flood-prone areas will be warned so that they can evacuate themselves and their belongings before the arrival of the flood. This can considerably reduce flood loss and damage and above all, the loss of human lives. Integrated Flood Analysis System (IFAS) model is a runoff analysis model converting rainfall into runoff for a given river basin. The simulation can be done using either ground or satellite-based rainfall to produce calculated discharge within the river. The calculated discharge is used to generate the flood inundation map within the catchment area for the selected flood event using Infowork RS.
Erich J. Plate
Full Text Available This paper demonstrates, by means of a systematic uncertainty analysis, that the use of outputs from more than one model can significantly improve conditional forecasts of discharges or water stages, provided the models are structurally different. Discharge forecasts from two models and the actual forecasted discharge are assumed to form a three-dimensional joint probability density distribution (jpdf, calibrated on long time series of data. The jpdf is decomposed into conditional probability density distributions (cpdf by means of Bayes formula, as suggested and explored by Krzysztofowicz in a series of papers. In this paper his approach is simplified to optimize conditional forecasts for any set of two forecast models. Its application is demonstrated by means of models developed in a study of flood forecasting for station Stung Treng on the middle reach of the Mekong River in South-East Asia. Four different forecast models were used and pairwise combined: forecast with no model, with persistence model, with a regression model, and with a rainfall-runoff model. Working with cpdfs requires determination of dependency among variables, for which linear regressions are required, as was done by Krzysztofowicz. His Bayesian approach based on transforming observed probability distributions of discharges and forecasts into normal distributions is also explored. Results obtained with his method for normal prior and likelihood distributions are identical to results from direct multiple regressions. Furthermore, it is shown that in the present case forecast accuracy is only marginally improved, if Weibull distributed basic data were converted into normally distributed variables.
Full Text Available The dynamic relationship between watershed characteristics and rainfall-runoff has been widely studied in recent decades. Since watershed rainfall-runoff is a non-stationary process, most deterministic flood forecasting approaches are ineffective without the assistance of adaptive algorithms. The purpose of this paper is to propose an effective flow forecasting system that integrates a rainfall forecasting model, watershed runoff model, and real-time updating algorithm. This study adopted a grey rainfall forecasting technique, based on existing hourly rainfall data. A geomorphology-based runoff model can be used for simulating impacts of the changing geo-climatic conditions on the hydrologic response of unsteady and non-linear watershed system, and flow updating algorithm were combined to estimate watershed runoff according to measured flow data. The proposed flood forecasting system was applied to three watersheds; one in the United States and two in Northern Taiwan. Four sets of rainfall-runoff simulations were performed to test the accuracy of the proposed flow forecasting technique. The results indicated that the forecast and observed hydrographs are in good agreement for all three watersheds. The proposed flow forecasting system could assist authorities in minimizing loss of life and property during flood events.
Hu, R.; Fang, F.; Salinas, P.; Pain, C. C.
Over the past few decades, urban floods have been gaining more attention due to their increase in frequency. To provide reliable flooding predictions in urban areas, various numerical models have been developed to perform high-resolution flood simulations. However, the use of high-resolution meshes across the whole computational domain causes a high computational burden. In this paper, a 2D control-volume and finite-element flood model using adaptive unstructured mesh technology has been developed. This adaptive unstructured mesh technique enables meshes to be adapted optimally in time and space in response to the evolving flow features, thus providing sufficient mesh resolution where and when it is required. It has the advantage of capturing the details of local flows and wetting and drying front while reducing the computational cost. Complex topographic features are represented accurately during the flooding process. For example, the high-resolution meshes around the buildings and steep regions are placed when the flooding water reaches these regions. In this work a flooding event that happened in 2002 in Glasgow, Scotland, United Kingdom has been simulated to demonstrate the capability of the adaptive unstructured mesh flooding model. The simulations have been performed using both fixed and adaptive unstructured meshes, and then results have been compared with those published 2D and 3D results. The presented method shows that the 2D adaptive mesh model provides accurate results while having a low computational cost.
Azad Wan Hazdy
Full Text Available Flood disaster occurs quite frequently in Malaysia and has been categorized as the most threatening natural disaster compared to landslides, hurricanes, tsunami, haze and others. A study by Department of Irrigation and Drainage (DID show that 9% of land areas in Malaysia are prone to flood which may affect approximately 4.9 million of the population. 2 Dimensional floods routing modelling demonstrate is turning out to be broadly utilized for flood plain display and is an extremely viable device for evaluating flood. Flood propagations can be better understood by simulating the flow and water level by using hydrodynamic modelling. The hydrodynamic flood routing can be recognized by the spatial complexity of the schematization such as 1D model and 2D model. It was found that most of available hydrological models for flood forecasting are more focus on short duration as compared to long duration hydrological model using the Probabilistic Distribution Moisture Model (PDM. The aim of this paper is to discuss preliminary findings on development of flood forecasting model using Probabilistic Distribution Moisture Model (PDM for Kelantan river basin. Among the findings discuss in this paper includes preliminary calibrated PDM model, which performed reasonably for the Dec 2014, but underestimated the peak flows. Apart from that, this paper also discusses findings on Soil Moisture Deficit (SMD and flood plain analysis. Flood forecasting is the complex process that begins with an understanding of the geographical makeup of the catchment and knowledge of the preferential regions of heavy rainfall and flood behaviour for the area of responsibility. Therefore, to decreases the uncertainty in the model output, so it is important to increase the complexity of the model.
Thiemig, V.; Roo, A.P.J. de
An overview of the current state of flood forecasting and early warning in Africa is provided in order to identify future user needs and research. Information was collected by reviewing previously published research in the scientific literature and from institutional websites. This information was
Fakhruddin, S.; Kawasaki, A.; Babel, M. S.; AIT
Early warning is a key element for disaster risk reduction. In recent decades, there has been a major advancement in medium range and seasonal forecasting. These could provide a great opportunity to improve early warning systems and advisories for early action for strategic and long term planning. This could result in increasing emphasis on proactive rather than reactive management of adverse consequences of flood events. This can be also very helpful for the agricultural sector by providing a diversity of options to farmers (e.g. changing cropping pattern, planting timing, etc.). An experimental medium range (1-10 days) flood forecasting model has been developed for Bangladesh which provides 51 set of discharge ensembles forecasts of one to ten days with significant persistence and high certainty. This could help communities (i.e. farmer) for gain/lost estimation as well as crop savings. This paper describe the application of ensembles probabilistic flood forecast at the community level for differential decision making focused on agriculture. The framework allows users to interactively specify the objectives and criteria that are germane to a particular situation, and obtain the management options that are possible, and the exogenous influences that should be taken into account before planning and decision making. risk and vulnerability assessment was conducted through community consultation. The forecast lead time requirement, users' needs, impact and management options for crops, livestock and fisheries sectors were identified through focus group discussions, informal interviews and questionnaire survey.
Pillosu, F. M.; Hewson, T.; Mazzetti, C.
Prediction of local extreme rainfall has historically been the remit of nowcasting and high resolution limited area modelling, which represent only limited areas, may not be spatially accurate, give reasonable results only for limited lead times (based statistical post-processing software ("ecPoint-Rainfall, ecPR", operational in 2017) that uses ECMWF Ensemble (ENS) output to deliver global probabilistic rainfall forecasts for points up to day 10. Firstly, ecPR applies a new notion of "remote calibration", which 1) allows us to replicate a multi-centennial training period using only one year of data, and 2) provides forecasts for anywhere in the world. Secondly, the software applies an understanding of how different rainfall generation mechanisms lead to different degrees of sub-grid variability in rainfall totals, and of where biases in the model can be improved upon. A long-term verification has shown that the post-processed rainfall has better reliability and resolution at every lead time if compared with ENS, and for large totals, ecPR outputs have the same skill at day 5 that the raw ENS has at day 1 (ROC area metric). ecPR could be used as input for hydrological models if its probabilistic output is modified accordingly to the inputs requirements for hydrological models. Indeed, ecPR does not provide information on where the highest total is likely to occur inside the gridbox, nor on the spatial distribution of rainfall values nearby. "Scenario forecasts" could be a solution. They are derived from locating the rainfall peak in sensitive positions (e.g. urban areas), and then redistributing the remaining quantities in the gridbox modifying traditional spatial correlation characterization methodologies (e.g. variogram analysis) in order to take account, for instance, of the type of rainfall forecast (stratiform, convective). Such an approach could be a turning point in the field of medium-range global real-time riverine flood forecasts. This presentation will
Verkade, J.S.; Werner, M.G.F.
Flood risk can be reduced by means of flood forecasting, warning and response systems (FFWRS). These systems include a forecasting sub-system which is imperfect, meaning that inherent uncertainties in hydrological forecasts may result in false alarms and missed events. This forecasting uncertainty
Huang, H.; Wang, X.; Zhang, S.; Liu, Y.
In recent years urban flood frequently occurred and seriously impacted city's normal operation, particular on transportation. The increase of urban flood could be attributed to many factors, such as the increase of impervious land surface and extreme precipitation, the decrease of surface storage capacity, poor maintenance of drainage utilities, and so on. In order to provide accurate and leading prediction on urban flooding, this study acquires precise urban topographic data via air-borne Lidar system, collects detailed underground drainage pipes, and installs in-situ monitoring networks on precipitation, water level, video record and traffic speed in the downtown area of Panyu District, Guangzhou, China. Based on the above data acquired, a urban flood model with EPA SWMM5 is established to simulate the flooding and inundation processes in the study area of 20 km2. The model is driven by the real-time precipitation data and calibrated by the water level data, which are converted to flooding volume with precise topographic data. After calibration, the model could be employed to conduct sensitivity analysis for investigating primary factors of urban flooding, and to simulate the flooding processes in different scenarios, which are beneficial to assessment of flooding risk and drainage capacity. This model is expected to provide real-time forecasting in emergency management.
Thierion, V.; Ayral, P.-A.; Angelini, V.; Sauvagnargues-Lesage, S.; Nativi, S.; Payrastre, O.
Flash flood events of south of France such as the 8th and 9th September 2002 in the Grand Delta territory caused important economic and human damages. Further to this catastrophic hydrological situation, a reform of flood warning services have been initiated (set in 2006). Thus, this political reform has transformed the 52 existing flood warning services (SAC) in 22 flood forecasting services (SPC), in assigning them territories more hydrological consistent and new effective hydrological forecasting mission. Furthermore, national central service (SCHAPI) has been created to ease this transformation and support local services in their new objectives. New functioning requirements have been identified: - SPC and SCHAPI carry the responsibility to clearly disseminate to public organisms, civil protection actors and population, crucial hydrologic information to better anticipate potential dramatic flood event, - a new effective hydrological forecasting mission to these flood forecasting services seems essential particularly for the flash floods phenomenon. Thus, models improvement and optimization was one of the most critical requirements. Initially dedicated to support forecaster in their monitoring mission, thanks to measuring stations and rainfall radar images analysis, hydrological models have to become more efficient in their capacity to anticipate hydrological situation. Understanding natural phenomenon occuring during flash floods mainly leads present hydrological research. Rather than trying to explain such complex processes, the presented research try to manage the well-known need of computational power and data storage capacities of these services. Since few years, Grid technology appears as a technological revolution in high performance computing (HPC) allowing large-scale resource sharing, computational power using and supporting collaboration across networks. Nowadays, EGEE (Enabling Grids for E-science in Europe) project represents the most important
Full Text Available Investigate the introduction of environmental considerations in public policy and urban interventions are complex. This article aims to observe the parameters that have been established in plans and projects for the management of storm and river water in urban watercourses located in a variety of cities, looking to raise some essential premises for public policy on urban drainage in the Brazilian context. It seeks to understand how these plans deal with floods and urban settlements in flood areas, and also explore the actions taken before, during and after extreme events. It could be say that adequately address drainage is primarily for institutional changes and, alongside the actions detailed throughout the article, for an investment of major consequence to allow decent housing to a significant portion of the population, an important set of environmental imprint actions.
Full Text Available The influence of initial state variables on flood forecasting accuracy by using conceptual hydrological models is analyzed in this paper and a novel flood forecasting method based on correction of initial state variables is proposed. The new method is abbreviated as ISVC (Initial State Variable Correction. The ISVC takes the residual between the measured and forecasted flows during the initial period of the flood event as the objective function, and it uses a particle swarm optimization algorithm to correct the initial state variables, which are then used to drive the flood forecasting model. The historical flood events of 11 watersheds in south China are forecasted and verified, and important issues concerning the ISVC application are then discussed. The study results show that the ISVC is effective and applicable in flood forecasting tasks. It can significantly improve the flood forecasting accuracy in most cases.
Löwe, Roland; Urich, Christian; Kulahci, Murat
An early flood warning system has been developed for urban catchments and is currently running in online operation in Copenhagen. The system is highly dependent on the quality of rainfall forecast inputs. An investigation of precipitation inputs from Radar Nowcast (RN), Numerical Weather Predicti...
Full Text Available This paper deals with the problem of analysing the coupling of meteorological meso-scale quantitative precipitation forecasts with distributed rainfall-runoff models to extend the forecasting horizon. Traditionally, semi-distributed rainfall-runoff models have been used for real time flood forecasting. More recently, increased computer capabilities allow the utilisation of distributed hydrological models with mesh sizes from tenths of metres to a few kilometres. On the other hand, meteorological models, providing the quantitative precipitation forecast, tend to produce average values on meshes ranging from slightly less than 10 to 200 kilometres. Therefore, to improve the quality of flood forecasts, the effects of coupling the meteorological and the hydrological models at different scales were analysed. A distributed hydrological model (TOPKAPI was developed and calibrated using a 1x1 km mesh for the case of the river Po closed at Ponte Spessa (catchment area c. 37000 km2. The model was then coupled with several other European meteorological models ranging from the Limited Area Models (provided by DMI and DWD with resolutions from 0.0625° * 0.0625°, to the ECMWF ensemble predictions with a resolution of 1.85° * 1.85°. Interesting results, describing the coupled model behaviour, are available for a meteorological extreme event in Northern Italy (Nov. 1994. The results demonstrate the poor reliability of the quantitative precipitation forecasts produced by meteorological models presently available; this is not resolved using the Ensemble Forecasting technique, when compared with results obtainable with measured rainfall.
Revilla-Romero, Beatriz; Shelton, Kay; Wood, Elizabeth; Berry, Robert; Bevington, John; Hankin, Barry; Lewis, Gavin; Gubbin, Andrew; Griffiths, Samuel; Barnard, Paul; Pinnell, Marc; Huyck, Charles
The hours and days immediately after a major flood event are often chaotic and confusing, with first responders rushing to mobilise emergency responders, provide alleviation assistance and assess loss to assets of interest (e.g., population, buildings or utilities). Preparations in advance of a forthcoming event are becoming increasingly important; early warning systems have been demonstrated to be useful tools for decision markers. The extent of damage, human casualties and economic loss estimates can vary greatly during an event, and the timely availability of an accurate flood extent allows emergency response and resources to be optimised, reduces impacts, and helps prioritise recovery. In the insurance sector, for example, insurers are under pressure to respond in a proactive manner to claims rather than waiting for policyholders to report losses. Even though there is a great demand for flood inundation extents and severity information in different sectors, generating flood footprints for large areas from hydraulic models in real time remains a challenge. While such footprints can be produced in real time using remote sensing, weather conditions and sensor availability limit their ability to capture every single flood event across the globe. In this session, we will present Flood Foresight (www.floodforesight.com), an operational tool developed to meet the universal requirement for rapid geographic information, before, during and after major riverine flood events. The tool provides spatial data with which users can measure their current or predicted impact from an event - at building, basin, national or continental scales. Within Flood Foresight, the Screening component uses global rainfall predictions to provide a regional- to continental-scale view of heavy rainfall events up to a week in advance, alerting the user to potentially hazardous situations relevant to them. The Forecasting component enhances the predictive suite of tools by providing a local
Ten Veldhuis, J.A.E.
Urban flood risk analyses suffer from a lack of quantitative historical data on flooding incidents. Data collection takes place on an ad hoc basis and is usually restricted to severe events. The resulting data deficiency renders quantitative assessment of urban flood risks uncertain. The study
Brooks, K.N.; Fallon, J.D.; Lorenz, D.L.; Stark, J.R.; Menard, Jason; Easter, K.W.; Perry, Jim
Floods result in great human disasters globally and nationally, causing an average of $4 billion of damages each year in the United States. Minnesota has its share of floods and flood damages, and the state has awarded nearly $278 million to local units of government for flood mitigation projects through its Flood Hazard Mitigation Grant Program. Since 1995, flood mitigation in the Red River Valley has exceeded $146 million. Considerable local and state funding has been provided to manage and mitigate problems of excess stormwater in urban areas, flooding of farmlands, and flood damages at road crossings. The cumulative costs involved with floods and flood mitigation in Minnesota are not known precisely, but it is safe to conclude that flood mitigation is a costly business. This chapter begins with a description of floods in Minneosta to provide examples and contrasts across the state. Background material is presented to provide a basic understanding of floods and flood processes, predication, and management and mitigation. Methods of analyzing and characterizing floods are presented because they affect how we respond to flooding and can influence relevant practices. The understanding and perceptions of floods and flooding commonly differ among those who work in flood forecasting, flood protection, or water resource mamnagement and citizens and businesses affected by floods. These differences can become magnified following a major flood, pointing to the need for better understanding of flooding as well as common language to describe flood risks and the uncertainty associated with determining such risks. Expectations of accurate and timely flood forecasts and our ability to control floods do not always match reality. Striving for clarity is important in formulating policies that can help avoid recurring flood damages and costs.
Mohammad, M.; Andras, B.
This study is to review the impact of climate change and land used on flooding through the Klang River and to compare the changes in the existing river system in Klang River Basin with the Storm water Management and Road Tunnel (SMART) which is now already operating in the city centre of Kuala Lumpur. Klang River Basin is the most urbanized region in Malaysia. More than half of the basin has been urbanized on the land that is prone to flooding. Numerous flood mitigation projects and studies have been carried out to enhance the existing flood forecasting and mitigation project. The objective of this study is to develop a hydrological model for flood forecasting in Klang Basin Malaysia. Hydrological modelling generally requires large set of input data and this is more often a challenge for a developing country. Due to this limitation, the Tropical Rainfall Measuring Mission (TRMM) rainfall measurement, initiated by the US space agency NASA and Japanese space agency JAXA was used in this study. TRMM data was transformed and corrected by quantile to quantile transformation. However, transforming the data based on ground measurement doesn't make any significant improvement and the statistical comparison shows only 10% difference. The conceptual HYMOD model was used in this study and calibrated using ROPE algorithm. But, using the whole time series of the observation period in this area resulted in insufficient performance. The depth function which used in ROPE algorithm are then used to identified and calibrated using only unusual event to observed the improvement and efficiency of the model.
Liguori, Sara; Rico-Ramirez, Miguel
, 2010. Review of the different sources of uncertainty in single polarization radar-based estimates of rainfall. Surveys in Geophysics 31: 107-129.  Rossa A, Liechti K, Zappa M, Bruen M, Germann U, Haase G, Keil C, Krahe P, 2011. The COST 731 Action: A review on uncertainty propagation in advanced hydrometeorological forecast systems. Atmospheric Research 100, 150-167.  Rossa A, Bruen M, Germann U, Haase G, Keil C, Krahe P, Zappa M, 2010. Overview and Main Results on the interdisciplinary effort in flood forecasting COST 731-Propagation of Uncertainty in Advanced Meteo-Hydrological Forecast Systems. Proceedings of Sixth European Conference on Radar in Meteorology and Hydrology ERAD 2010.  Germann U, Berenguer M, Sempere-Torres D, Zappa M, 2009. REAL - ensemble radar precipitation estimation for hydrology in a mountainous region. Quarterly Journal of the Royal Meteorological Society 135: 445-456.  Bowler NEH, Pierce CE, Seed AW, 2006. STEPS: a probabilistic precipitation forecasting scheme which merges and extrapolation nowcast with downscaled NWP. Quarterly Journal of the Royal Meteorological Society 132: 2127-2155.  Zappa M, Rotach MW, Arpagaus M, Dorninger M, Hegg C, Montani A, Ranzi R, Ament F, Germann U, Grossi G et al., 2008. MAP D-PHASE: real-time demonstration of hydrological ensemble prediction systems. Atmospheric Science Letters 9, 80-87.  Liguori S, Rico-Ramirez MA. Quantitative assessment of short-term rainfall forecasts from radar nowcasts and MM5 forecasts. Hydrological Processes, accepted article. DOI: 10.1002/hyp.8415  Liguori S, Rico-Ramirez MA, Schellart ANA, Saul AJ, 2012. Using probabilistic radar rainfall nowcasts and NWP forecasts for flow prediction in urban catchments. Atmospheric Research 103: 80-95.  Harrison DL, Driscoll SJ, Kitchen M, 2000. Improving precipitation estimates from weather radar using quality control and correction techniques. Meteorological Applications 7: 135-144.  Harrison DL, Scovell RW, Kitchen
Nam Do Hoai
Full Text Available Downscaling global weather prediction model outputs to individual locations or local scales is a common practice for operational weather forecast in order to correct the model outputs at subgrid scales. This paper presents an empirical-statistical downscaling method for precipitation prediction which uses a feed-forward multilayer perceptron (MLP neural network. The MLP architecture was optimized by considering physical bases that determine the circulation of atmospheric variables. Downscaled precipitation was then used as inputs to the super tank model (runoff model for flood prediction. The case study was conducted for the Thu Bon River Basin, located in Central Vietnam. Study results showed that the precipitation predicted by MLP outperformed that directly obtained from model outputs or downscaled using multiple linear regression. Consequently, flood forecast based on the downscaled precipitation was very encouraging. It has demonstrated as a robust technology, simple to implement, reliable, and universal application for flood prediction through the combination of downscaling model and super tank model.
Schmitz, G. H.; Cullmann, J.
SummaryThe Process Modelling and Artificial Intelligence for Online Flood Forecasting (PAI-OFF) methodology combines the reliability of physically based, hydrologic/hydraulic modelling with the operational advantages of artificial intelligence. These operational advantages are extremely low computation times and straightforward operation. The basic principle of the methodology is to portray process models by means of ANN. We propose to train ANN flood forecasting models with synthetic data that reflects the possible range of storm events. To this end, establishing PAI-OFF requires first setting up a physically based hydrologic model of the considered catchment and - optionally, if backwater effects have a significant impact on the flow regime - a hydrodynamic flood routing model of the river reach in question. Both models are subsequently used for simulating all meaningful and flood relevant storm scenarios which are obtained from a catchment specific meteorological data analysis. This provides a database of corresponding input/output vectors which is then completed by generally available hydrological and meteorological data for characterizing the catchment state prior to each storm event. This database subsequently serves for training both a polynomial neural network (PoNN) - portraying the rainfall-runoff process - and a multilayer neural network (MLFN), which mirrors the hydrodynamic flood wave propagation in the river. These two ANN models replace the hydrological and hydrodynamic model in the operational mode. After presenting the theory, we apply PAI-OFF - essentially consisting of the coupled "hydrologic" PoNN and "hydrodynamic" MLFN - to the Freiberger Mulde catchment in the Erzgebirge (Ore-mountains) in East Germany (3000 km 2). Both the demonstrated computational efficiency and the prediction reliability underline the potential of the new PAI-OFF methodology for online flood forecasting.
Helena M. Ramos
Full Text Available Sustainability is crucial to the urban zones, especially related to the water management, which is vulnerable to flood occurrence. This research applies the procedure contemplated by the Soil Conservation Service (SCS to determine the generated volumes when the impervious areas can exceed the drainage capacity of existing pluvial water networks. Several computational simulations were developed for the current scenario of an existing basin in Lisbon. Using CivilStorm software from Bentley Systems (Bentley EMEA, Bentley Systems International Limited, Dublin, Ireland, it enabled the evaluation of the volumes of flood peaks and the hydraulic behavior of a small hydrographic basin in the continuation of an urbanization process, considering the modification of its superficial impervious parts and the growth of the urbanized area. Several measures are suggested to solve the limited capacity of the existing drainage system. This study analyzes the efficiency of the application of constructive measures, pondering the viability of their effectiveness, individually and combined. The option that best minimizes the effects of the urbanization is the combination of different structural measures, in particular retention ponds, storage blocks, ditches and specific drainage interventions in some parts of the network.
Yaw Manful, Desmond; He, Yi; Cloke, Hannah; Pappenberger, Florian; Li, Zhijia; Wetterhall, Fredrik; Huang, Yingchun; Hu, Yuzhong
Flooding is a wide spread and devastating natural disaster worldwide. Floods that took place in the last decade in China were ranked the worst amongst recorded floods worldwide in terms of the number of human fatalities and economic losses (Munich Re-Insurance). Rapid economic development and population expansion into low lying flood plains has worsened the situation. Current conventional flood prediction systems in China are neither suited to the perceptible climate variability nor the rapid pace of urbanization sweeping the country. Flood prediction, from short-term (a few hours) to medium-term (a few days), needs to be revisited and adapted to changing socio-economic and hydro-climatic realities. The latest technology requires implementation of multiple numerical weather prediction systems. The availability of twelve global ensemble weather prediction systems through the ‘THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a good opportunity for an effective state-of-the-art early forecasting system. A prototype of a Novel Flood Early Warning System (NEWS) using the TIGGE database is tested in the Huai River basin in east-central China. It is the first early flood warning system in China that uses the massive TIGGE database cascaded with river catchment models, the Xinanjiang hydrologic model and a 1-D hydraulic model, to predict river discharge and flood inundation. The NEWS algorithm is also designed to provide web-based services to a broad spectrum of end-users. The latter presents challenges as both databases and proprietary codes reside in different locations and converge at dissimilar times. NEWS will thus make use of a ready-to-run grid system that makes distributed computing and data resources available in a seamless and secure way. An ability to run or function on different operating systems and provide an interface or front that is accessible to broad spectrum of end-users is additional requirement. The aim is to achieve robust interoperability
Khan, U. T.
Globally floods are one of the most devastating natural disasters and improved flood forecasting methods are essential for better flood protection in urban areas. Given the availability of high resolution real-time datasets for flood variables (e.g. streamflow and precipitation) in many urban areas, data-driven models have been effectively used to predict peak flow rates in river; however, the selection of input parameters for these types of models is often subjective. Additionally, the inherit uncertainty associated with data models along with errors in extreme event observations means that uncertainty quantification is essential. Addressing these concerns will enable improved flood forecasting methods and provide more accurate flood risk assessments. In this research, a new type of data-driven model, a quasi-real-time updating fuzzy neural network is developed to predict peak flow rates in urban riverine watersheds. A possibility-to-probability transformation is first used to convert observed data into fuzzy numbers. A possibility theory based training regime is them used to construct the fuzzy parameters and the outputs. A new entropy-based optimisation criterion is used to train the network. Two existing methods to select the optimum input parameters are modified to account for fuzzy number inputs, and compared. These methods are: Entropy-Wavelet-based Artificial Neural Network (EWANN) and Combined Neural Pathway Strength Analysis (CNPSA). Finally, an automated algorithm design to select the optimum structure of the neural network is implemented. The overall impact of each component of training this network is to replace the traditional ad hoc network configuration methods, with one based on objective criteria. Ten years of data from the Bow River in Calgary, Canada (including two major floods in 2005 and 2013) are used to calibrate and test the network. The EWANN method selected lagged peak flow as a candidate input, whereas the CNPSA method selected lagged
Struma is a mountain river flowing from North to South, from Bulgaria through Greece up to the Aegean Sea. It generates flush floods of snow melt - rainfall type mainly in the late spring. Flood forecasting there is needed to improve the flood mitigation measures at the Bulgarian territory of the basin as well as for effective reservoir management downstream Bulgarian border, secure flood handling at Greek territory and generally decrease the flood hazard. The paper summarizes the range of activities in the basin including: - the installation of automatic telemetric hydro meteorological observation network; - review of the results of relevant past projects; - analysis of historical hydro meteorological data; - design and calibration of flood forecasting models; - demonstrating the possibility to issue flood warnings with certain lead time and accuracy; - recent efforts to increase the lead time of the hydrological forecasts, applying forecasts from High Resolution Limited Area meteorological models and other activities in the frame of the EC 5th FP EFFS project.(Author)
Full Text Available The Liane River is a small costal river, famous for its floods, which can affect the city of Boulogne-sur-Mer. Due to the complexity of land cover and hydrologic processes, a black-box non-linear modelling was chosen using neural networks. The multilayer perceptron model, known for its property of universal approximation is thus chosen. Four models were designed, each one for one forecasting horizon using rainfall forecasts: 24h, 12h, 6h, 3h. The desired output of the model is original: it represents the maximal value of the water level respectively 24h, 12h, 6h, 3h ahead. Working with best forecasts of rain (the observed ones during the event in the past, on the major flood of the database in test set, the model provides excellent forecasts. Nash criteria calculated for the four lead times are 0.98 (3h, 0.97 (6h, 0.91 (12h, 0.89 (24h. Designed models were thus estimated as efficient enough to be implemented in a specific tool devoted to real time operational use. The software tool is described hereafter: designed in Java, it presents a friendly interface allowing applying various scenarios of future rainfalls, and a graphical visualization of the predicted maximum water levels and their associated real time observed values.
Winston T. L. Chow
Full Text Available We investigated flooding patterns in the urbanised city-state of Singapore through a multimethod approach combining station precipitation data with archival newspaper and governmental records; changes in flash floods frequencies or reported impacts of floods towards Singapore society were documented. We subsequently discussed potential flooding impacts in the context of urban vulnerability, based on future urbanisation and forecasted precipitation projections for Singapore. We find that, despite effective flood management, (i significant increases in reported flash flood frequency occurred in contemporary (post-2000 relative to preceding (1984–1999 periods, (ii these flash floods coincide with more localised, “patchy” storm events, (iii storms in recent years are also more intense and frequent, and (iv floods result in low human casualties but have high economic costs via insurance damage claims. We assess that Singapore presently has low vulnerability to floods vis-à-vis other regional cities largely due to holistic flood management via consistent and successful infrastructural development, widespread flood monitoring, and effective advisory platforms. We conclude, however, that future vulnerabilities may increase from stresses arising from physical exposure to climate change and from demographic sensitivity via rapid population growth. Anticipating these changes is potentially useful in maintaining the high resilience of Singapore towards this hydrometeorological hazard.
Berni, N.; Pandolfo, C.; Stelluti, M.; Ponziani, F.; Viterbo, A.
The hydrometeorological alert office (called "Decentrate Functional Centre" - CFD) of Umbria Region, in central Italy, is the office that provides technical tools able to support decisions when significant flood/landslide events occur, furnishing 24h support for the whole duration of the emergency period, according to the national directive DPCM 27 February 2004 concerning the "Operating concepts for functional management of national and regional alert system during flooding and landslide events for civil protection activities purposes" that designs, within the Italian Civil Defence Emergency Management System, a network of 21 regional Functional Centres coordinated by a central office at the National Civil Protection Department in Rome. Due to its "linking" role between Civil Protection "real time" activities and environmental/planning "deferred time" ones, the Centre is in charge to acquire and collect both real time and quasi-static data: quantitative data from monitoring networks (hydrometeorological stations, meteo radar, ...), meteorological forecasting models output, Earth Observation data, hydraulic and hydrological simulation models, cartographic and thematic GIS data (vectorial and raster type), planning studies related to flooding areas mapping, dam managing plans during flood events, non instrumental information from direct control of "territorial presidium". A detailed procedure for the management of critical events was planned, also in order to define the different role of various authorities and institutions involved. Tiber River catchment, of which Umbria region represents the main upper-medium portion, includes also regional trans-boundary issues very important to cope with, especially for what concerns large dam behavior and management during heavy rainfall. The alert system is referred to 6 different warning areas in which the territory has been divided into and based on a threshold system of three different increasing critical levels according
Mousa, Mustafa; Zhang, Xiangliang; Claudel, Christian
Floods are the most common type of natural disaster. Often leading to loss of lives and properties in the thousands yearly. Among these events, urban flash floods are particularly deadly because of the short timescales on which they occur, and because of the population density of cities. Since most flood casualties are caused by a lack of information on the impending flood (type, location, severity), sensing these events is critical to generate accurate and detailed warnings and short term forecasts. However, no dedicated flash flood sensing systems, that could monitor the propagation of flash floods, in real time, currently exist in cities. In the present paper, firstly a new sensing device that can simultaneously monitor urban flash floods and traffic congestion has been presented. This sensing device is based on the combination of ultrasonic range-finding with remote temperature sensing, and can sense both phenomena with a high degree of accuracy, using a combination of L1-regularized reconstruction and artificial neural networks to process measurement data. Secondly, corresponding algorithms have been implemented on a low-power wireless sensor platform, and their performance in water level estimation in a 6 months test involving four different sensors is illustrated. The results demonstrate that urban water levels can be reliably estimated with error less than 2 cm, and that the preprocessing and machine learning schemes can run in real-time on currently available wireless sensor platforms.
Floods are the most common type of natural disaster. Often leading to loss of lives and properties in the thousands yearly. Among these events, urban flash floods are particularly deadly because of the short timescales on which they occur, and because of the population density of cities. Since most flood casualties are caused by a lack of information on the impending flood (type, location, severity), sensing these events is critical to generate accurate and detailed warnings and short term forecasts. However, no dedicated flash flood sensing systems, that could monitor the propagation of flash floods, in real time, currently exist in cities. In the present paper, firstly a new sensing device that can simultaneously monitor urban flash floods and traffic congestion has been presented. This sensing device is based on the combination of ultrasonic range-finding with remote temperature sensing, and can sense both phenomena with a high degree of accuracy, using a combination of L1-regularized reconstruction and artificial neural networks to process measurement data. Secondly, corresponding algorithms have been implemented on a low-power wireless sensor platform, and their performance in water level estimation in a 6 months test involving four different sensors is illustrated. The results demonstrate that urban water levels can be reliably estimated with error less than 2 cm, and that the preprocessing and machine learning schemes can run in real-time on currently available wireless sensor platforms.
Full Text Available Countermeasures to urban flooding should consider long-term perspectives, because climate change impacts are unpredictable and complex. Urban green spaces have emerged as a potential option to reduce urban flood risks, and their effectiveness has been highlighted in notable urban water management studies. In this study, flooded areas in Seoul, Korea, were divided into four flooded area types by cluster analysis based on topographic and physical characteristics and verified using discriminant analysis. After division by flooded area type, logistic regression analysis was performed to determine how the flooding probability changes with variations in green space area. Type 1 included regions where flooding occurred in a drainage basin that had a flood risk management infrastructure (FRMI. In Type 2, the slope was steep; the TWI (Topographic Wetness Index was relatively low; and soil drainage was favorable. Type 3 represented the gentlest sloping areas, and these were associated with the highest TWI values. In addition, these areas had the worst soil drainage. Type 4 had moderate slopes, imperfect soil drainage and lower than average TWI values. We found that green spaces exerted a considerable influence on urban flooding probabilities in Seoul, and flooding probabilities could be reduced by over 50% depending on the green space area and the locations where green spaces were introduced. Increasing the area of green spaces was the most effective method of decreasing flooding probability in Type 3 areas. In Type 2 areas, the maximum hourly precipitation affected the flooding probability significantly, and the flooding probability in these areas was high despite the extensive green space area. These findings can contribute towards establishing guidelines for urban spatial planning to respond to urban flooding.
J. S. Verkade
Full Text Available Flood risk can be reduced by means of flood forecasting, warning and response systems (FFWRS. These systems include a forecasting sub-system which is imperfect, meaning that inherent uncertainties in hydrological forecasts may result in false alarms and missed events. This forecasting uncertainty decreases the potential reduction of flood risk, but is seldom accounted for in estimates of the benefits of FFWRSs. In the present paper, a method to estimate the benefits of (imperfect FFWRSs in reducing flood risk is presented. The method is based on a hydro-economic model of expected annual damage (EAD due to flooding, combined with the concept of Relative Economic Value (REV. The estimated benefits include not only the reduction of flood losses due to a warning response, but also consider the costs of the warning response itself, as well as the costs associated with forecasting uncertainty. The method allows for estimation of the benefits of FFWRSs that use either deterministic or probabilistic forecasts. Through application to a case study, it is shown that FFWRSs using a probabilistic forecast have the potential to realise higher benefits at all lead-times. However, it is also shown that provision of warning at increasing lead-time does not necessarily lead to an increasing reduction of flood risk, but rather that an optimal lead-time at which warnings are provided can be established as a function of forecast uncertainty and the cost-loss ratio of the user receiving and responding to the warning.
Tuyls, Damian Murla; Thorndahl, Søren Liedtke; Rasmussen, Michael Robdrup
Intense rainfall in urban areas can often generate severe flood impacts. Consequently, it is crucial to design systems to minimize potential flood damages. Traditional, simple design of urban drainage systems assumes agreement between rainfall return period and its consequent flood return period......; however, this does not always apply. Hydraulic infrastructures found in urban drainage systems can increase system heterogeneity and perturb the impact of severe rainfall response. In this study, a surface flood return period assessment was carried out at Lystrup (Denmark), which has received the impact...... of flooding in recent years. A 35 years' rainfall dataset together with a coupled 1D/2D surface and network model was used to analyse and assess flood return period response. Results show an ambiguous relation between rainfall and flood return periods indicating that linear rainfall–runoff relationships will...
Ravazzani, Giovanni; Amengual, Arnau; Ceppi, Alessandro; Romero, Romualdo; Homar, Victor; Mancini, Marco
Analysis of forecasting strategies that can provide a tangible basis for flood early warning procedures and mitigation measures over the Western Mediterranean region is one of the fundamental motivations of the European HyMeX programme. Here, we examine a set of hydro-meteorological episodes that affected the Milano urban area for which the complex flood protection system of the city did not completely succeed before the occurred flash-floods. Indeed, flood damages have exponentially increased in the area during the last 60 years, due to industrial and urban developments. Thus, the improvement of the Milano flood control system needs a synergism between structural and non-structural approaches. The flood forecasting system tested in this work comprises the Flash-flood Event-based Spatially distributed rainfall-runoff Transformation, including Water Balance (FEST-WB) and the Weather Research and Forecasting (WRF) models, in order to provide a hydrological ensemble prediction system (HEPS). Deterministic and probabilistic quantitative precipitation forecasts (QPFs) have been provided by WRF model in a set of 48-hours experiments. HEPS has been generated by combining different physical parameterizations (i.e. cloud microphysics, moist convection and boundary-layer schemes) of the WRF model in order to better encompass the atmospheric processes leading to high precipitation amounts. We have been able to test the value of a probabilistic versus a deterministic framework when driving Quantitative Discharge Forecasts (QDFs). Results highlight (i) the benefits of using a high-resolution HEPS in conveying uncertainties for this complex orographic area and (ii) a better simulation of the most of extreme precipitation events, potentially enabling valuable probabilistic QDFs. Hence, the HEPS copes with the significant deficiencies found in the deterministic QPFs. These shortcomings would prevent to correctly forecast the location and timing of high precipitation rates and
Full Text Available In many countries, industrialization has led to rapid urbanization. Increased frequency of urban flooding is one consequence of the expansion of urban areas which can seriously affect the productivity and livelihoods of urban residents. Therefore, it is of vital importance to study the effects of rainfall and urban flooding on traffic congestion and driver behavior. In this study, a comprehensive method to analyze the influence of urban flooding on traffic congestion was developed. First, a flood simulation was conducted to predict the spatiotemporal distribution of flooding based on Storm Water Management Model (SWMM and TELAMAC-2D. Second, an agent-based model (ABM was used to simulate driver behavior during a period of urban flooding, and a car-following model was established. Finally, in order to study the mechanisms behind how urban flooding affects traffic congestion, the impact of flooding on urban traffic was investigated based on a case study of the urban area of Lishui, China, covering an area of 4.4 km2. It was found that for most events, two-hour rainfall has a certain impact on traffic congestion over a five-hour period, with the greatest impact during the hour following the cessation of the rain. Furthermore, the effects of rainfall with 10- and 20-year return periods were found to be similar and small, whereas the effects with a 50-year return period were obvious. Based on a combined analysis of hydrology and transportation, the proposed methods and conclusions could help to reduce traffic congestion during flood seasons, to facilitate early warning and risk management of urban flooding, and to assist users in making informed decisions regarding travel.
Ten Veldhuis, J.A.E.; Clemens, F.H.L.R.; Van Gelder, P.H.A.J.M.
Traditional methods to evaluate flood risk mostly focus on storm events as the main cause of flooding. Fault tree analysis is a technique that is able to model all potential causes of flooding and to quantify both the overall probability of flooding and the contributions of all causes of flooding to
Shibuo, Yoshihiro; Ikoma, Eiji; Lawford, Peter; Oyanagi, Misa; Kanauchi, Shizu; Koudelova, Petra; Kitsuregawa, Masaru; Koike, Toshio
While availability of hydrological- and hydrometeorological data shows growing tendency and advanced modeling techniques are emerging, such newly available data and advanced models may not always be applied in the field of decision-making. In this study we present an integrated system of ensemble streamflow forecast (ESP) and virtual dam simulator, which is designed to support river and dam manager's decision making. The system consists of three main functions: real time hydrological model, ESP model, and dam simulator model. In the real time model, the system simulates current condition of river basins, such as soil moisture and river discharges, using LSM coupled distributed hydrological model. The ESP model takes initial condition from the real time model's output and generates ESP, based on numerical weather prediction. The dam simulator model provides virtual dam operation and users can experience impact of dam control on remaining reservoir volume and downstream flood under the anticipated flood forecast. Thus the river and dam managers shall be able to evaluate benefit of priori dam release and flood risk reduction at the same time, on real time basis. Furthermore the system has been developed under the concept of data and models integration, and it is coupled with Data Integration and Analysis System (DIAS) - a Japanese national project for integrating and analyzing massive amount of observational and model data. Therefore it has advantage in direct use of miscellaneous data from point/radar-derived observation, numerical weather prediction output, to satellite imagery stored in data archive. Output of the system is accessible over the web interface, making information available with relative ease, e.g. from ordinary PC to mobile devices. We have been applying the system to the Upper Tone region, located northwest from Tokyo metropolitan area, and we show application example of the system in recent flood events caused by typhoons.
Full Text Available Flood managers have traditionally used probabilistic models to assess potential flood risk for strategic planning and non-operational applications. Computational restrictions on data volumes and simulation times have meant that information on the risk of flooding has not been available for operational flood forecasting purposes. In practice, however, the operational flood manager has probabilistic questions to answer, which are not completely supported by the outputs of traditional, deterministic flood forecasting systems. In a collaborative approach, HR Wallingford and Deltares have developed methods, tools and techniques to extend existing flood forecasting systems with elements of strategic flood risk analysis, including probabilistic failure analysis, two dimensional flood spreading simulation and the analysis of flood impacts and consequences. This paper presents the results of the application of these new operational flood risk management tools to a pilot catchment in the UK. It discusses the problems of performing probabilistic flood risk assessment in real time and how these have been addressed in this study. It also describes the challenges of the communication of risk to operational flood managers and to the general public, and how these new methods and tools can provide risk-based supporting evidence to assist with this process.
Ozdemir, Hasan; Neal, Jeffrey; Bates, Paul; Döker, Fatih
Urban flood events are increasing in frequency and severity as a consequence of several factors such as reduced infiltration capacities due to continued watershed development, increased construction in flood prone areas due to population growth, the possible amplification of rainfall intensity due to climate change, sea level rise which threatens coastal development, and poorly engineered flood control infrastructure (Gallegos et al., 2009). These factors will contribute to increased urban flood risk in the future, and as a result improved modelling of urban flooding according to different causative factor has been identified as a research priority (Gallegos et al., 2009; Ozdemir et al. 2013). The flooding disaster caused by dam failures is always a threat against lives and properties especially in urban environments. Therefore, the prediction of dynamics of dam-break flows plays a vital role in the forecast and evaluation of flooding disasters, and is of long-standing interest for researchers. Flooding occurred on the Ayamama River (Istanbul-Turkey) due to high intensity rainfall and dam-breaching of Ata Pond in 9th September 2009. The settlements, industrial areas and transportation system on the floodplain of the Ayamama River were inundated. Therefore, 32 people were dead and millions of Euros economic loses were occurred. The aim of this study is 1 and 2-Dimensional flood modelling of the Ata Pond breaching using HEC-RAS and LISFLOOD-Roe models and comparison of the model results using the real flood extent. The HEC-RAS model solves the full 1-D Saint Venant equations for unsteady open channel flow whereas LISFLOOD-Roe is the 2-D shallow water model which calculates the flow according to the complete Saint Venant formulation (Villanueva and Wright, 2006; Neal et al., 2011). The model consists a shock capturing Godunov-type scheme based on the Roe Riemann solver (Roe, 1981). 3 m high resolution Digital Surface Model (DSM), natural characteristics of the pond
Full Text Available Precipitation is the core data input to hydrological forecasting. The uncertainty in precipitation forecast data can lead to poor performance of predictive hydrological models. Radar-based precipitation measurement offers advantages over ground-based measurement in the quantitative estimation of temporal and spatial aspects of precipitation, but errors inherent in this method will still act to reduce the performance. Using data from White Lotus River of Hubei Province, China, five methods were used to assimilate radar rainfall data transformed from the classified Z-R relationship, and the postassimilation data were compared with precipitation measured by rain gauges. The five sets of assimilated rainfall data were then used as input to the Xinanjiang model. The effect of precipitation data input error on runoff simulation was analyzed quantitatively by disturbing the input data using the Breeding of Growing Modes method. The results of practical application demonstrated that the statistical weight integration and variational assimilation methods were superior. The corresponding performance in flood hydrograph prediction was also better using the statistical weight integration and variational methods compared to the others. It was found that the errors of radar rainfall data disturbed by the Breeding of Growing Modes had a tendency to accumulate through the hydrological model.
Thielen-Del Pozo, J.; Pappenberger, F.; Salamon, P.; Bogner, K.; Burek, P.; de Roo, A.
Flood forecasting systems form a key part of ‘preparedness' strategies for disastrous floods and provide hydrological services, civil protection authorities and the public with information of upcoming events. Provided the warning leadtime is sufficiently long, adequate preparatory actions can be taken to efficiently reduce the impacts of the flooding. Because of the specific characteristics of each catchment, varying data availability and end-user demands, the design of the best flood forecasting system may differ from catchment to catchment. However, despite the differences in concept and data needs, there is one underlying issue that spans across all systems. There has been an growing awareness and acceptance that uncertainty is a fundamental issue of flood forecasting and needs to be dealt with at the different spatial and temporal scales as well as the different stages of the flood generating processes. Today, operational flood forecasting centres change increasingly from single deterministic forecasts to probabilistic forecasts with various representations of the different contributions of uncertainty. The move towards these so-called Hydrological Ensemble Prediction Systems (HEPS) in flood forecasting represents the state of the art in forecasting science, following on the success of the use of ensembles for weather forecasting (Buizza et al., 2005) and paralleling the move towards ensemble forecasting in other related disciplines such as climate change predictions. The use of HEPS has been internationally fostered by initiatives such as "The Hydrologic Ensemble Prediction Experiment" (HEPEX), created with the aim to investigate how best to produce, communicate and use hydrologic ensemble forecasts in hydrological short-, medium- und long term prediction of hydrological processes. The advantages of quantifying the different contributions of uncertainty as well as the overall uncertainty to obtain reliable and useful flood forecasts also for extreme events
This abstract presents research from two studies investigating urban flood perceptions and mitigative behaviours of private individuals in Canada. The first study, completed in July, 2006, investigated perceptions of overland flooding and sewer backup resulting from extreme rainfall events in Peterborough, Ontario. The second, completed in November, 2007, investigated sewer backup perceptions of homeowners in Edmonton, Alberta and Toronto, Ontario. The research studies sought to explore: Hazard and risk perceptions of individuals affected by overland flooding and sewer backup; Knowledge of mitigative options, and mitigative actions taken by individual residents to reduce the risk of basement flood damage; Attributions of responsibility for urban flood damages; Awareness of municipal actions designed to reduce urban flood risk; Satisfaction with the cost sharing tools of insurance and government relief.
Murla, Damian; Thorndahl, Søren Liedtke
Intense rainfall can often cause severe floods, especially in urbanized areas, where population density or large impermeable areas are found. In this context, floods can generate a direct impact in a social-environmental-economic viewpoint. Traditionally, in design of Urban Drainage Systems (UDS......), correlation between return period (RP) of a given rainfall and RP of its consequent flood has been assumed to be linear (e.g.DS/EN752 (2008)). However, this is not always the case. Complex UDS, where diverse hydraulic infrastructures are often found, increase the heterogeneity of system response, which may...... cause an alteration of the mentioned correlation. Consequently, reliability on future urban planning, design and resilience against floods may be also affected by this misassumption. In this study, an assessment of surface flood RP across rainfall RP has been carried out at Lystrup, a urbanized...
Zischg, Andreas Paul; Mosimann, Markus; Weingartner, Rolf
A key aspect of disaster prevention is flood discharge forecasting which is used for early warning and therefore as a decision support for intervention forces. Hereby, the phase between the issued forecast and the time when the expected flood occurs is crucial for an optimal planning of the intervention. Typically, river discharge forecasts cover the regional level only, i.e. larger catchments. However, it is important to note that these forecasts are not useable directly for specific target groups on local level because these forecasts say nothing about the consequences of the predicted flood in terms of affected areas, number of exposed residents and houses. For this, on one hand simulations of the flooding processes and on the other hand data of vulnerable objects are needed. Furthermore, flood modelling in a high spatial and temporal resolution is required for robust flood loss estimation. This is a resource-intensive task from a computing time point of view. Therefore, in real-time applications flood modelling in 2D is not suited. Thus, forecasting flood losses in the short-term (6h-24h in advance) requires a different approach. Here, we propose a method to downscale the river discharge forecast to a spatially-explicit flood loss forecast. The principal procedure is to generate as many flood scenarios as needed in advance to represent the flooded areas for all possible flood hydrographs, e.g. very high peak discharges of short duration vs. high peak discharges with high volumes. For this, synthetic flood hydrographs were derived from the hydrologic time series. Then, the flooded areas of each scenario were modelled with a 2D flood simulation model. All scenarios were intersected with the dataset of vulnerable objects, in our case residential, agricultural and industrial buildings with information about the number of residents, the object-specific vulnerability, and the monetary value of the objects. This dataset was prepared by a data-mining approach. For each
Bennett, J.; David, R. E.; Wang, Q.; Li, M.; Shrestha, D. L.
Flood forecasting in Australia has historically relied on deterministic forecasting models run only when floods are imminent, with considerable forecaster input and interpretation. These now co-existed with a continually available 7-day streamflow forecasting service (also deterministic) aimed at operational water management applications such as environmental flow releases. The 7-day service is not optimised for flood prediction. We describe progress on developing a system for ensemble streamflow forecasting that is suitable for both flood prediction and water management applications. Precipitation uncertainty is handled through post-processing of Numerical Weather Prediction (NWP) output with a Bayesian rainfall post-processor (RPP). The RPP corrects biases, downscales NWP output, and produces reliable ensemble spread. Ensemble precipitation forecasts are used to force a semi-distributed conceptual rainfall-runoff model. Uncertainty in precipitation forecasts is insufficient to reliably describe streamflow forecast uncertainty, particularly at shorter lead-times. We characterise hydrological prediction uncertainty separately with a 4-stage error model. The error model relies on data transformation to ensure residuals are homoscedastic and symmetrically distributed. To ensure streamflow forecasts are accurate and reliable, the residuals are modelled using a mixture-Gaussian distribution with distinct parameters for the rising and falling limbs of the forecast hydrograph. In a case study of the Murray River in south-eastern Australia, we show ensemble predictions of floods generally have lower errors than deterministic forecasting methods. We also discuss some of the challenges in operationalising short-term ensemble streamflow forecasts in Australia, including meeting the needs for accurate predictions across all flow ranges and comparing forecasts generated by event and continuous hydrological models.
Fakhruddin, Shamsul; Ballio, Francesco; Menoni, Scira
Early warning is a key element for disaster risk reduction. In recent decades, major advancements have been made in medium range and seasonal flood forecasting. This progress provides a great opportunity to reduce agriculture damage and improve advisories for early action and planning for flood hazards. This approach can facilitate proactive rather than reactive management of the adverse consequences of floods. In the agricultural sector, for instance, farmers can take a diversity of options such as changing cropping patterns, applying fertilizer, irrigating and changing planting timing. An experimental medium range (1-10 day) and seasonal (20-25 days) flood forecasting model has been developed for Thailand and Bangladesh. It provides 51 sets of discharge ensemble forecasts of 1-10 days with significant persistence and high certainty and qualitative outlooks for 20-25 days. This type of forecast could assist farmers and other stakeholders for differential preparedness activities. These ensembles probabilistic flood forecasts have been customized based on user-needs for community-level application focused on agriculture system. The vulnerabilities of agriculture system were calculated based on exposure, sensitivity and adaptive capacity. Indicators for risk and vulnerability assessment were conducted through community consultations. The forecast lead time requirement, user-needs, impacts and management options for crops were identified through focus group discussions, informal interviews and community surveys. This paper illustrates potential applications of such ensembles for probabilistic medium range and seasonal flood forecasts in a way that is not commonly practiced globally today.
Cole Steven J.
Full Text Available Major UK floods over the last decade have motivated significant technological and scientific advances in operational flood forecasting and warning. New joint forecasting centres between the national hydrological and meteorological operating agencies have been formed that issue a daily, national Flood Guidance Statement (FGS to the emergency response community. The FGS is based on a Flood Risk Matrix approach that is a function of potential impact severity and likelihood. It has driven an increased demand for robust, accurate and timely forecast and alert information on fluvial and surface water flooding along with impact assessments. The Grid-to-Grid (G2G distributed hydrological model has been employed across Britain at a 1km resolution to support the FGS. Novel methods for linking dynamic gridded estimates of river flow and surface runoff with more detailed offline flood risk maps have been developed to obtain real-time probabilistic forecasts of potential impacts, leading to operational trials. Examples of the national-scale G2G application are provided along with case studies of forecast flood impact from (i an operational Surface Water Flooding (SWF trial during the Glasgow 2014 Commonwealth Games, (ii SWF developments under the Natural Hazards Partnership over England & Wales, and (iii fluvial applications in Scotland.
Murphy Alexandra T.
Full Text Available Flooding events that impede railway infrastructure can cause severe travel delays for the general public and large fines in delayed minutes for the rail industry. Early warnings of flood inundation can give more time to implement mitigation measures which help reduce cancellations, delays and fines. Initial work is reported on the development of a real-time flood inundation forecasting and mapping system for the Cowley Bridge track area near Exeter, UK. This location is on one of the main access routes to South West England and has suffered major floods in the past resulting in significant transport impacts. Flood forecasting systems in the UK mainly forecast river level/flow rather than extent and depth of flood inundation. Here, the development of a chain of coupled models is discussed that link rainfall to river flow, river level and flood extent for the rail track area relating to Cowley Bridge. Historical events are identified to test model performance in predicting inundation of railway infrastructure. The modelling system will operate alongside a series of in-situ sensors chosen to enhance the flood mapping forecasting system. Sensor data will support offline model calibration/verification and real-time data assimilation as well as monitoring flood conditions to inform track closure decisions.
Full Text Available Quantifying the uncertainty of flood forecasts by ensemble methods is becoming increasingly important for operational purposes. The aim of this paper is to examine how the ensemble distribution of precipitation forecasts propagates in the catchment system, and to interpret the flood forecast probabilities relative to the forecast errors. We use the 622 km2 Kamp catchment in Austria as an example where a comprehensive data set, including a 500 yr and a 1000 yr flood, is available. A spatially-distributed continuous rainfall-runoff model is used along with ensemble and deterministic precipitation forecasts that combine rain gauge data, radar data and the forecast fields of the ALADIN and ECMWF numerical weather prediction models. The analyses indicate that, for long lead times, the variability of the precipitation ensemble is amplified as it propagates through the catchment system as a result of non-linear catchment response. In contrast, for lead times shorter than the catchment lag time (e.g. 12 h and less, the variability of the precipitation ensemble is decreased as the forecasts are mainly controlled by observed upstream runoff and observed precipitation. Assuming that all ensemble members are equally likely, the statistical analyses for five flood events at the Kamp showed that the ensemble spread of the flood forecasts is always narrower than the distribution of the forecast errors. This is because the ensemble forecasts focus on the uncertainty in forecast precipitation as the dominant source of uncertainty, and other sources of uncertainty are not accounted for. However, a number of analyses, including Relative Operating Characteristic diagrams, indicate that the ensemble spread is a useful indicator to assess potential forecast errors for lead times larger than 12 h.
Inna Pivovarova; Daria Sokolova; Artur Batyrov; Vadim Kuzmin; Ngoc Anh Tran; DinhKha Dang; Kirill V. Shemanaev
Accurate and timely flash floods forecasting, especially, in ungauged and poorly gauged basins, is one of the most important and challenging problems to be solved by the international hydrological community. In changing climate and variable anthropogenic impact on river basins, as well as due to low density of surface hydrometeorological network, flash flood forecasting based on “traditional” physically based, or conceptual, or statistical hydrological models often becomes inefficient. Unfort...
Rakovec, Oldrich; Weerts, Albrecht; Sumihar, Julius; Uijlenhoet, Remko
and assimilation and is suitable to be connected to any kind of environmental model. This setup is embedded in the Delft Flood Early Warning System (Delft-FEWS, Werner et al., 2013) for making all simulations and forecast runs and handling of all hydrological and meteorological data. References: Evensen, G. (2009), Data Assimilation: The Ensemble Kalman Filter, Springer, doi:10.1007/978-3-642-03711-5. OpenDA (2013), The OpenDA data-assimilation toolbox, www.openda.org, (last access: 1 November 2013). OpenStreams (2013), OpenStreams, www.openstreams.nl, (last access: 1 November 2013). Sakov, P., G. Evensen, and L. Bertino (2010), Asynchronous data assimilation with the EnKF, Tellus, Series A: Dynamic Meteorology and Oceanography, 62(1), 24-29, doi:10.1111/j.1600-0870.2009.00417.x. Werner, M., J. Schellekens, P. Gijsbers, M. van Dijk, O. van den Akker, and K. Heynert (2013), The Delft-FEWS flow forecasting system, Environ. Mod. & Soft., 40(0), 65-77, doi: http://dx.doi.org/10.1016/j.envsoft.2012.07.010.
Full Text Available Flood forecasting, warning and emergency response are important components of flood management. Currently, the model-based prediction of discharge and/or water level in a river is common practice for operational flood forecasting. Based on the prediction of these values decisions about specific emergency measures are made within emergency response. However, the information provided for decision support is often restricted to pure hydrological or hydraulic aspects of a flood. Information about weak sections within the flood defences, flood prone areas and assets at risk in the protected areas are rarely used in current early warning and response systems. This information is often available for strategic planning, but is not in an appropriate format for operational purposes. This paper presents the extension of existing flood forecasting systems with elements of strategic flood risk analysis, such as probabilistic failure analysis, two dimensional flood spreading simulation and the analysis of flood impacts and consequences. This paper presents the first results from two prototype applications of the new developed concept: The first prototype is applied to the Rotterdam area situated in the western part of the Netherlands. The second pilot study focusses on a rural area between the cities of Mantua and Ferrara along the Po river (Italy.
Full Text Available Early flood warning and real-time monitoring systems play a key role in flood risk reduction and disaster response decisions. Global-scale flood forecasting and satellite-based flood detection systems are currently operating, however their reliability for decision-making applications needs to be assessed. In this study, we performed comparative evaluations of several operational global flood forecasting and flood detection systems, using 10 major flood events recorded over 2012–2014. Specifically, we evaluated the spatial extent and temporal characteristics of flood detections from the Global Flood Detection System (GFDS and the Global Flood Awareness System (GloFAS. Furthermore, we compared the GFDS flood maps with those from NASA’s two Moderate Resolution Imaging Spectroradiometer (MODIS sensors. Results reveal that: (1 general agreement was found between the GFDS and MODIS flood detection systems, (2 large differences exist in the spatio-temporal characteristics of the GFDS detections and GloFAS forecasts, and (3 the quantitative validation of global flood disasters in data-sparse regions is highly challenging. Overall, satellite remote sensing provides useful near real-time flood information that can be useful for risk management. We highlight the known limitations of global flood detection and forecasting systems, and propose ways forward to improve the reliability of large-scale flood monitoring tools.
Neumann, Jessica; Arnal, Louise; Magnusson, Linus; Cloke, Hannah
Seasonal river flow forecasts are important for many aspects of the water sector including flood forecasting, water supply, hydropower generation and navigation. In addition to short term predictions, seasonal forecasts have the potential to realise higher benefits through more optimal and consistent decisions. Their operational use however, remains a challenge due to uncertainties posed by the initial hydrologic conditions (e.g. soil moisture, groundwater levels) and seasonal climate forcings (mainly forecasts of precipitation and temperature), leading to a decrease in skill with increasing lead times. Here we present a stakeholder-led case study for the Thames catchment (UK), currently being undertaken as part of the H2020 IMPREX project. The winter of 2013-14 was the wettest on record in the UK; driven by 12 major Atlantic depressions, the Thames catchment was subject to compound (concurrent) flooding from fluvial and groundwater sources. Focusing on the 2013-14 floods, this study aims to see whether increased skill in meteorological input translates through to more accurate forecasting of compound flood events at seasonal timescales in the Thames catchment. An earlier analysis of the ECMWF System 4 (S4) seasonal meteorological forecasts revealed that it did not skilfully forecast the extreme event of winter 2013-14. This motivated the implementation of an atmospheric experiment by the ECMWF to force the S4 to more accurately represent the low-pressure weather conditions prevailing in winter 2013-14 . Here, we used both the standard and the "improved" S4 seasonal meteorological forecasts to force the EFAS (European Flood Awareness System) LISFLOOD hydrological model. Both hydrological forecasts were started on the 1st of November 2013 and run for 4 months of lead time to capture the peak of the 2013-14 flood event. Comparing the seasonal hydrological forecasts produced with both meteorological forcing data will enable us to assess how the improved meteorology
Winkler, Daniel; Zischg, Jonatan; Rauch, Wolfgang
For communicating urban flood risk to authorities and the public, a realistic three-dimensional visual display is frequently more suitable than detailed flood maps. Virtual reality could also serve to plan short-term flooding interventions. We introduce here an alternative approach for simulating three-dimensional flooding dynamics in large- and small-scale urban scenes by reaching out to computer graphics. This approach, denoted 'particle in cell', is a particle-based CFD method that is used to predict physically plausible results instead of accurate flow dynamics. We exemplify the approach for the real flooding event in July 2016 in Innsbruck.
Courdent, Vianney Augustin Thomas; Vezzaro, Luca; Mikkelsen, Peter Steen
Global Real Time Control (RTC) of urban drainage system is increasingly seen as cost-effective solution in order to respond to increasing performance demand (e.g. reduction of Combined Sewer Overflow, protection of sensitive areas as bathing water etc.). The Dynamic Overflow Risk Assessment (DORA......) strategy was developed to operate Urban Drainage Systems (UDS) in order to minimize the expected overflow risk by considering the water volume presently stored in the drainage network, the expected runoff volume based on a 2-hours radar forecast model and an estimated uncertainty of the runoff forecast....... However, such temporal horizon (1-2 hours) is relatively short when used for the operation of large storage facilities, which may require a few days to be emptied. This limits the performance of the optimization and control in reducing combined sewer overflow and in preparing for possible flooding. Based...
Courdent, Vianney Augustin Thomas; Vezzaro, Luca; Mikkelsen, Peter Steen
Global Real Time Control (RTC) of urban drainage system is increasingly seen as cost-effective solution in order to respond to increasing performance demand (e.g. reduction of Combined Sewer Overflow, protection of sensitive areas as bathing water etc.). The Dynamic Overflow Risk Assessment (DORA......) strategy was developed to operate Urban Drainage Systems (UDS) in order to minimize the expected overflow risk by considering the water volume presently stored in the drainage network, the expected runoff volume based on a 2-hours radar forecast model and an estimated uncertainty of the runoff forecast....... However, such temporal horizon (1-2 hours) is relatively short when used for the operation of large storage facilities, which may require a few days to be emptied. This limits the performance of the optimization and control in reducing combined sewer overflow and in preparing for possible flooding. Based...
White, C. J.; Franks, S. W.; McEvoy, D.
Meteorological and hydrological centres around the world are looking at ways to improve their capacity to be able to produce and deliver skilful and reliable forecasts of high-impact extreme rainfall and flooding events on a range of prediction timescales (e.g. sub-daily, daily, multi-week, seasonal). Making improvements to extended-range rainfall and flood forecast models, assessing forecast skill and uncertainty, and exploring how to apply flood forecasts and communicate their benefits to decision-makers are significant challenges facing the forecasting and water resources management communities. This paper presents some of the latest science and initiatives from Australia on the development, application and communication of extreme rainfall and flood forecasts on the extended-range "subseasonal-to-seasonal" (S2S) forecasting timescale, with a focus on risk-based decision-making, increasing flood risk awareness and preparedness, capturing uncertainty, understanding human responses to flood forecasts and warnings, and the growing adoption of "climate services". The paper also demonstrates how forecasts of flood events across a range of prediction timescales could be beneficial to a range of sectors and society, most notably for disaster risk reduction (DRR) activities, emergency management and response, and strengthening community resilience. Extended-range S2S extreme flood forecasts, if presented as easily accessible, timely and relevant information are a valuable resource to help society better prepare for, and subsequently cope with, extreme flood events.
C. J. White
Full Text Available Meteorological and hydrological centres around the world are looking at ways to improve their capacity to be able to produce and deliver skilful and reliable forecasts of high-impact extreme rainfall and flooding events on a range of prediction timescales (e.g. sub-daily, daily, multi-week, seasonal. Making improvements to extended-range rainfall and flood forecast models, assessing forecast skill and uncertainty, and exploring how to apply flood forecasts and communicate their benefits to decision-makers are significant challenges facing the forecasting and water resources management communities. This paper presents some of the latest science and initiatives from Australia on the development, application and communication of extreme rainfall and flood forecasts on the extended-range "subseasonal-to-seasonal" (S2S forecasting timescale, with a focus on risk-based decision-making, increasing flood risk awareness and preparedness, capturing uncertainty, understanding human responses to flood forecasts and warnings, and the growing adoption of "climate services". The paper also demonstrates how forecasts of flood events across a range of prediction timescales could be beneficial to a range of sectors and society, most notably for disaster risk reduction (DRR activities, emergency management and response, and strengthening community resilience. Extended-range S2S extreme flood forecasts, if presented as easily accessible, timely and relevant information are a valuable resource to help society better prepare for, and subsequently cope with, extreme flood events.
Full Text Available Possible risks in reservoir flood control and regulation cannot be objectively assessed by deterministic flood forecasts, resulting in the probability of reservoir failure. We demonstrated a risk analysis of reservoir flood routing calculation accounting for inflow forecast uncertainty in a sub-basin of Huaihe River, China. The Xinanjiang model was used to provide deterministic flood forecasts, and was combined with the Hydrologic Uncertainty Processor (HUP to quantify reservoir inflow uncertainty in the probability density function (PDF form. Furthermore, the PDFs of reservoir water level (RWL and the risk rate of RWL exceeding a defined safety control level could be obtained. Results suggested that the median forecast (50th percentiles of HUP showed better agreement with observed inflows than the Xinanjiang model did in terms of the performance measures of flood process, peak, and volume. In addition, most observations (77.2% were bracketed by the uncertainty band of 90% confidence interval, with some small exceptions of high flows. Results proved that this framework of risk analysis could provide not only the deterministic forecasts of inflow and RWL, but also the fundamental uncertainty information (e.g., 90% confidence band for the reservoir flood routing calculation.
This study examined urban geomorphic conditions that lead to flooding in urban areas of ... the elimination of vegetation cover as well as deficient drainage networks ... Department of Geography & Resource Development, University of Ghana, Legon, .... A number of case studies from different parts of the world dealing.
Xu, Wei; Peng, Yong
This research presents a new classified real-time flood forecasting framework. In this framework, historical floods are classified by a K-means cluster according to the spatial and temporal distribution of precipitation, the time variance of precipitation intensity and other hydrological factors. Based on the classified results, a rough set is used to extract the identification rules for real-time flood forecasting. Then, the parameters of different categories within the conceptual hydrological model are calibrated using a genetic algorithm. In real-time forecasting, the corresponding category of parameters is selected for flood forecasting according to the obtained flood information. This research tests the new classified framework on Guanyinge Reservoir and compares the framework with the traditional flood forecasting method. It finds that the performance of the new classified framework is significantly better in terms of accuracy. Furthermore, the framework can be considered in a catchment with fewer historical floods.
Wood, A. W.; Clark, M. P.; Nijssen, B.
Operational flood forecasting is currently undergoing a major transformation. Most national flood forecasting services have relied for decades on lumped, highly calibrated conceptual hydrological models running on local office computing resources, providing deterministic streamflow predictions at gauged river locations that are important to stakeholders and emergency managers. A variety of recent technological advances now make it possible to run complex, high-to-hyper-resolution models for operational hydrologic prediction over large domains, and the US National Weather Service is now attempting to use hyper-resolution models to create new forecast services and products. Yet other `increased-complexity' forecasting strategies also exist that pursue different tradeoffs between model complexity (i.e., spatial resolution, physics) and streamflow forecast system objectives. There is currently a pressing need for a greater understanding in the hydrology community of the opportunities, challenges and tradeoffs associated with these different forecasting approaches, and for a greater participation by the hydrology community in evaluating, guiding and implementing these approaches. Intermediate-resolution forecast systems, for instance, use distributed land surface model (LSM) physics but retain the agility to deploy ensemble methods (including hydrologic data assimilation and hindcast-based post-processing). Fully coupled numerical weather prediction (NWP) systems, another example, use still coarser LSMs to produce ensemble streamflow predictions either at the model scale or after sub-grid scale runoff routing. Based on the direct experience of the authors and colleagues in research and operational forecasting, this presentation describes examples of different streamflow forecast paradigms, from the traditional to the recent hyper-resolution, to illustrate the range of choices facing forecast system developers. We also discuss the degree to which the strengths and
Murla, Damian; Willems, Patrick
Floods in urban areas as a consequence of sewer capacity exceedance receive increased attention because of trends in urbanization (increased population density and impermeability of the surface) and climate change. Despite the strong recent developments in numerical modeling of water systems, urban surface flood modeling is still a major challenge. Whereas very advanced and accurate flood modeling systems are in place and operation by many river authorities in support of flood management along rivers, this is not yet the case in urban water management. Reasons include the small scale of the urban inundation processes, the need to have very high resolution topographical information available, and the huge computational demands. Urban drainage related inundation modeling requires a 1D full hydrodynamic model of the sewer network to be coupled with a 2D surface flood model. To reduce the computational times, 0D (flood cones), 1D/quasi-2D surface flood modeling approaches have been developed and applied in some case studies. In this research, a nested 1D/2D hydraulic model has been developed for an urban catchment at the city of Gent (Belgium), linking the underground sewer (minor system) with the overland surface (major system). For the overland surface flood modelling, comparison was made of 0D, 1D/quasi-2D and full 2D approaches. The approaches are advanced by considering nested 1D-2D approaches, including infiltration in the green city areas, and allowing the effects of surface storm water storage to be simulated. An optimal nested combination of three different mesh resolutions was identified; based on a compromise between precision and simulation time for further real-time flood forecasting, warning and control applications. Main streets as mesh zones together with buildings as void regions constitute one of these mesh resolution (3.75m2 - 15m2); they have been included since they channel most of the flood water from the manholes and they improve the accuracy of
Matori, Abd Nasir; Yusof, Khamaruzaman Wan; Hashim, Mustafa Ahmad; Lawal, Dano Umar; Balogun, Abdul-Lateef
Various flood influencing factors such as rainfall, geology, slope gradient, land use, soil type, drainage density, temperature etc. are generally considered for flood hazard assessment. However, lack of appropriate handling/integration of data from different sources is a challenge that can make any spatial forecasting difficult and inaccurate. Availability of accurate flood maps and thorough understanding of the subsurface conditions can adequately enhance flood disasters management. This study presents an approach that attempts to provide a solution to this drawback by combining Geographic Information System (GIS)-based Analytic Hierarchy Process (AHP) model as spatial forecasting tools. In achieving the set objectives, spatial forecasting of flood susceptible zones in the study area was made. A total number of five set of criteria/factors believed to be influencing flood generation in the study area were selected. Priority weights were assigned to each criterion/factor based on Saaty's nine point scale of preference and weights were further normalized through the AHP. The model was integrated into a GIS system in order to produce a flood forecasting map
Dottori, Francesco; Kalas, Milan; Lorini, Valerio; Wania, Annett; Pappenberger, Florian; Salamon, Peter; Ramos, Maria Helena; Cloke, Hannah; Castillo, Carlos
Between May and June 2016, France was hit by severe floods, particularly in the Loire and Seine river basins. In this work, we use this case study to test an innovative framework for flood forecasting, mapping and monitoring. More in detail, the system integrates in real-time two components of the Copernicus Emergency mapping services, namely the European Flood Awareness System and the satellite-based Rapid Mapping, with new procedures for rapid risk assessment and social media and news monitoring. We explore in detail the performance of each component of the system, demonstrating the improvements in respect to stand-alone flood forecasting and monitoring systems. We show how the performances of the forecasting component can be refined using the real-time feedback from social media monitoring to identify which areas were flooded, to evaluate the flood intensity, and therefore to correct impact estimations. Moreover, we show how the integration with impact forecast and social media monitoring can improve the timeliness and efficiency of satellite based emergency mapping, and reduce the chances of missing areas where flooding is already happening. These results illustrate how the new integrated approach leads to a better and earlier decision making and a timely evaluation of impacts.
Lee, S. V.; Lee, M. J.; Lee, C.; Yoon, J. H.; Chae, S. H.
The frequency and intensity of floods are increasing worldwide as recent climate change progresses gradually. Flood management should be policy-oriented in urban municipalities due to the characteristics of urban areas with a lot of damage. Therefore, the purpose of this study is to prepare a flood susceptibility map by using data mining model and make a policy suggestion on administrative measures of urban flood. Therefore, we constructed a spatial database by collecting relevant factors including the topography, geology, soil and land use data of the representative city, Seoul, the capital city of Korea. Flood susceptibility map was constructed by applying the data mining models of random forest and boosted tree model to input data and existing flooded area data in 2010. The susceptibility map has been validated using the 2011 flood area data which was not used for training. The predictor importance value of each factor to the results was calculated in this process. The distance from the water, DEM and geology showed a high predictor importance value which means to be a high priority for flood preparation policy. As a result of receiver operating characteristic (ROC), random forest model showed 78.78% and 79.18% accuracy of regression and classification and boosted tree model showed 77.55% and 77.26% accuracy of regression and classification, respectively. The results show that the flood susceptibility maps can be applied to flood prevention and management, and it also can help determine the priority areas for flood mitigation policy by providing useful information to policy makers.
Ricciardi, Giuseppe; Montani, Andrea; Paccagnella, Tiziana; Pecora, Silvano; Tonelli, Fabrizio
The Po basin is the largest and most economically important river-basin in Italy. Extreme hydrological events, including floods, flash floods and droughts, are expected to become more severe in the next future due to climate change, and related ground effects are linked both with environmental and social resilience. A Warning Operational Center (WOC) for hydrological event management was created in Emilia Romagna region. In the last years, the WOC faced challenges in legislation, organization, technology and economics, achieving improvements in forecasting skill and information dissemination. Since 2005, an operational forecasting and modelling system for flood modelling and forecasting has been implemented, aimed at supporting and coordinating flood control and emergency management on the whole Po basin. This system, referred to as FEWSPo, has also taken care of environmental aspects of flood forecast. The FEWSPo system has reached a very high level of complexity, due to the combination of three different hydrological-hydraulic chains (HEC-HMS/RAS - MIKE11 NAM/HD, Topkapi/Sobek), with several meteorological inputs (forecasted - COSMOI2, COSMOI7, COSMO-LEPS among others - and observed). In this hydrological and meteorological ensemble the management of the relative predictive uncertainties, which have to be established and communicated to decision makers, is a debated scientific and social challenge. Real time activities face professional, modelling and technological aspects but are also strongly interrelated with organization and human aspects. The authors will report a case study using the operational flood forecast hydro-meteorological ensemble, provided by the MIKE11 chain fed by COSMO_LEPS EQPF. The basic aim of the proposed approach is to analyse limits and opportunities of the long term forecast (with a lead time ranging from 3 to 5 days), for the implementation of low cost actions, also looking for a well informed decision making and the improvement of
Uprety, M.; Dugar, S.; Gautam, D.; Kanel, D.; Kshetri, M.; Kharbuja, R. G.; Acharya, S. H.
Advances in flood forecasting have provided opportunities for humanitarian responders to employ a range of preparedness activities at different forecast time horizons. Yet, the science of prediction is less understood and realized across the humanitarian landscape, and often preparedness plans are based upon average level of flood risk. Working under the remit of Forecast Based Financing (FbF), we present a pilot from Nepal on how available flood and weather forecast products are informing specific pre-emptive actions in the local preparedness and response plans, thereby supporting government stakeholders and humanitarian agencies to take early actions before an impending flood event. In Nepal, forecasting capabilities are limited but in a state of positive flux. Whilst local flood forecasts based upon rainfall-runoff models are yet to be operationalized, streamflow predictions from Global Flood Awareness System (GLoFAS) can be utilized to plan and implement preparedness activities several days in advance. Likewise, 3-day rainfall forecasts from Nepal Department of Hydrology and Meteorology (DHM) can further inform specific set of early actions for potential flash floods due to heavy precipitation. Existing community based early warning systems in the major river basins of Nepal are utilizing real time monitoring of water levels and rainfall together with localised probabilistic flood forecasts which has increased warning lead time from 2-3 hours to 7-8 hours. Based on these available forecast products, thresholds and trigger levels have been determined for different flood scenarios. Matching these trigger levels and assigning responsibilities to relevant actors for early actions, a set of standard operating procedures (SOPs) are being developed, broadly covering general preparedness activities and science informed anticipatory actions for different forecast lead times followed by the immediate response activities. These SOPs are currently being rolled out and
Full Text Available Ensemble forecasts aim at framing the uncertainties of the potential future development of the hydro-meteorological situation. A probabilistic evaluation can be used to communicate forecast uncertainty to decision makers. Here an operational system for ensemble based flood forecasting is presented, which combines forecasts from the European COSMO-LEPS, SRNWP-PEPS and COSMO-DE prediction systems. A multi-model lagged average super-ensemble is generated by recombining members from different runs of these meteorological forecast systems. A subset of the super-ensemble is selected based on a priori model weights, which are obtained from ensemble calibration. Flood forecasts are simulated by the conceptual rainfall-runoff-model ArcEGMO. Parameter uncertainty of the model is represented by a parameter ensemble, which is a priori generated from a comprehensive uncertainty analysis during model calibration. The use of a computationally efficient hydrological model within a flood management system allows us to compute the hydro-meteorological model chain for all members of the sub-ensemble. The model chain is not re-computed before new ensemble forecasts are available, but the probabilistic assessment of the output is updated when new information from deterministic short range forecasts or from assimilation of measured data becomes available. For hydraulic modelling, with the desired result of a probabilistic inundation map with high spatial resolution, a replacement model can help to overcome computational limitations. A prototype of the developed framework has been applied for a case study in the Mulde river basin. However these techniques, in particular the probabilistic assessment and the derivation of decision rules are still in their infancy. Further research is necessary and promising.
Water managers and planners require accurate water demand forecasts over the short-, medium- and long-term for many purposes. These range from assessing water supply needs over spatial and temporal patterns to optimizing future investments and planning future allocations across competing sectors. This study surveys the empirical literature on the urban water demand forecasting using the meta-analytical approach. Specifically, using more than 600 estimates, a meta-regression analysis is conducted to identify explanations of cross-studies variation in accuracy of urban water demand forecasting. Our study finds that accuracy depends significantly on study characteristics, including demand periodicity, modeling method, forecasting horizon, model specification and sample size. The meta-regression results remain robust to different estimators employed as well as to a series of sensitivity checks performed. The importance of these findings lies in the conclusions and implications drawn out for regulators and policymakers and for academics alike. Copyright © 2016. Published by Elsevier Ltd.
Full Text Available In Mediterranean Europe, flash flooding is one of the most devastating hazards in terms of loss of human life and infrastructures. Over the last two decades, flash floods have caused damage costing a billion Euros in France alone. One of the problems of flash floods is that warning times are very short, leaving typically only a few hours for civil protection services to act. This study investigates if operationally available short-range numerical weather forecasts together with a rainfall-runoff model can be used for early indication of the occurrence of flash floods.
One of the challenges in flash flood forecasting is that the watersheds are typically small, and good observational networks of both rainfall and discharge are rare. Therefore, hydrological models are difficult to calibrate and the simulated river discharges cannot always be compared with ground measurements. The lack of observations in most flash flood prone basins, therefore, necessitates the development of a method where the excess of the simulated discharge above a critical threshold can provide the forecaster with an indication of potential flood hazard in the area, with lead times of the order of weather forecasts.
This study is focused on the Cévennes-Vivarais region in the Southeast of the Massif Central in France, a region known for devastating flash floods. This paper describes the main aspects of using numerical weather forecasting for flash flood forecasting, together with a threshold – exceedance. As a case study the severe flash flood event which took place on 8–9 September 2002 has been chosen.
Short-range weather forecasts, from the Lokalmodell of the German national weather service, are used as input for the LISFLOOD model, a hybrid between a conceptual and physically based rainfall-runoff model. Results of the study indicate that high resolution operational weather forecasting combined with a rainfall-runoff model could be useful to
Hartnett, Michael; Olbert, Agnieszka; Nash, Stephen
The hydrodynamic modelling of rapid flood events due to extreme climatic events in urban environment is both a complex and challenging task. The horizontal resolution necessary to resolve complexity of urban flood dynamics is a critical issue; the presence of obstacles of varying shapes and length scales, gaps between buildings and the complex geometry of the city such as slopes affect flow paths and flood levels magnitudes. These small scale processes require a high resolution grid to be modelled accurately (2m or less, Olbert et al., 2015; Hunter et al., 2008; Brown et al., 2007) and, therefore, altimetry data of at least the same resolution. Along with availability of high-resolution LiDAR data and computational capabilities, as well as state of the art nested modelling approaches, these problems can now be overcome. Flooding and drying, domain definition, frictional resistance and boundary descriptions are all important issues to be addressed when modelling urban flooding. In recent years, the number of urban flood models dramatically increased giving a good insight into various modelling problems and solutions (Mark et al., 2004; Mason et al., 2007; Fewtrell et al., 2008; Shubert et al., 2008). Despite extensive modelling work conducted for fluvial (e.g. Mignot et al., 2006; Hunter et al., 2008; Yu and Lane, 2006) and coastal mechanisms of flooding (e.g. Gallien et al., 2011; Yang et al., 2012), the amount of investigations into combined coastal-fluvial flooding is still very limited (e.g. Orton et al., 2012; Lian et al., 2013). This is surprising giving the extent of flood consequences when both mechanisms occur simultaneously, which usually happens when they are driven by one process such as a storm. The reason for that could be the fact that the likelihood of joint event is much smaller than those of any of the two contributors occurring individually, because for fast moving storms the rainfall-driven fluvial flood arrives usually later than the storm surge
Gaitan, Santiago; ten Veldhuis, Marie-claire; van de Giesen, Nick
Cities worldwide are challenged by increasing urban flood risks. Precise and realistic measures are required to decide upon investment to reduce their impacts. Obvious flooding factors affecting flood risk include sewer systems performance and urban topography. However, currently implemented sewer and topographic models do not provide realistic predictions of local flooding occurrence during heavy rain events. Assessing other factors such as spatially distributed rainfall and socioeconomic characteristics may help to explain probability and impacts of urban flooding. Several public databases were analyzed: complaints about flooding made by citizens, rainfall depths (15 min and 100 Ha spatio-temporal resolution), grids describing number of inhabitants, income, and housing price (1Ha and 25Ha resolution); and buildings age. Data analysis was done using Python and GIS programming, and included spatial indexing of data, cluster analysis, and multivariate regression on the complaints. Complaints were used as a proxy to characterize flooding impacts. The cluster analysis, run for all the variables except the complaints, grouped part of the grid-cells of central Amsterdam into a highly differentiated group, covering 10% of the analyzed area, and accounting for 25% of registered complaints. The configuration of the analyzed variables in central Amsterdam coincides with a high complaint count. Remaining complaints were evenly dispersed along other groups. An adjusted R2 of 0.38 in the multivariate regression suggests that explaining power can improve if additional variables are considered. While rainfall intensity explained 4% of the incidence of complaints, population density and building age significantly explained around 20% each. Data mining of public databases proved to be a valuable tool to identify factors explaining variability in occurrence of urban pluvial flooding, though additional variables must be considered to fully explain flood risk variability.
Thorndahl, Søren Liedtke; Rasmussen, Michael R.
Model based short-term forecasting of urban storm water runoff can be applied in realtime control of drainage systems in order to optimize system capacity during rain and minimize combined sewer overflows, improve wastewater treatment or activate alarms if local flooding is impending. A novel onl....... The radar rainfall extrapolation (nowcast) limits the lead time of the system to two hours. In this paper, the model set-up is tested on a small urban catchment for a period of 1.5 years. The 50 largest events are presented....... online system, which forecasts flows and water levels in real-time with inputs from extrapolated radar rainfall data, has been developed. The fully distributed urban drainage model includes auto-calibration using online in-sewer measurements which is seen to improve forecast skills significantly...
Arcorace, Mauro; Silvestro, Francesco; Rudari, Roberto; Boni, Giorgio; Dell'Oro, Luca; Bjorgo, Einar
Most flood prone areas in the globe are mainly located in developing countries where making communities more flood resilient is a priority. Despite different flood forecasting initiatives are now available from academia and research centers, what is often missing is the connection between the timely hazard detection and the community response to warnings. In order to bridge the gap between science and decision makers, UN agencies play a key role on the dissemination of information in the field and on capacity-building to local governments. In this context, having a reliable global early warning system in the UN would concretely improve existing in house capacities for Humanitarian Response and the Disaster Risk Reduction. For those reasons, UNITAR-UNOSAT has developed together with USGS and CIMA Foundation a Global Flood EWS called "Flood-FINDER". The Flood-FINDER system is a modelling chain which includes meteorological, hydrological and hydraulic models that are accurately linked to enable the production of warnings and forecast inundation scenarios up to three weeks in advance. The system is forced with global satellite derived precipitation products and Numerical Weather Prediction outputs. The modelling chain is based on the "Continuum" hydrological model and risk assessments produced for GAR2015. In combination with existing hydraulically reconditioned SRTM data and 1D hydraulic models, flood scenarios are derived at multiple scales and resolutions. Climate and flood data are shared through a Web GIS integrated platform. First validation of the modelling chain has been conducted through a flood hindcasting test case, over the Chao Phraya river basin in Thailand, using multi temporal satellite-based analysis derived for the exceptional flood event of 2011. In terms of humanitarian relief operations, the EO-based services of flood mapping in rush mode generally suffer from delays caused by the time required for their activation, programming, acquisitions and
Defining issue The river inundations are the most common and destructive natural hazards in Ukraine. Among non-structural flood management and protection measures a creation of the Early Flood Warning System is extremely important to be able to timely recognize dangerous situations in the flood-prone areas. Hydrometeorological information and forecasts are a core importance in this system. The primary factors affecting reliability and a lead - time of forecasts include: accuracy, speed and reliability with which real - time data are collected. The existing individual conception of monitoring and forecasting resulted in a need in reconsideration of the concept of integrated monitoring and forecasting approach - from "sensors to database and forecasters". Result presentation The Project: "Development of Flood Monitoring and Forecasting in the Ukrainian part of the Dniester River Basin" is presented. The project is developed by the Ukrainian Hydrometeorological Service in a conjunction with the Water Management Agency and the Energy Company "Ukrhydroenergo". The implementation of the Project is funded by the Ukrainian Government and the World Bank. The author is nominated as the responsible person for coordination of activity of organizations involved in the Project. The term of the Project implementation: 2012 - 2014. The principal objectives of the Project are: a) designing integrated automatic hydrometeorological measurement network (including using remote sensing technologies); b) hydrometeorological GIS database construction and coupling with electronic maps for flood risk assessment; c) interface-construction classic numerical database -GIS and with satellite images, and radar data collection; d) providing the real-time data dissemination from observation points to forecasting centers; e) developing hydrometeoroogical forecasting methods; f) providing a flood hazards risk assessment for different temporal and spatial scales; g) providing a dissemination of
Liu, Z.; Yang, D.; Hu, J.
China is extremely frequent food disasters hit countries, annual flood season flash floods triggered by rainfall, mudslides, landslides have caused heavy casualties and property losses, not only serious threaten the lives of the masses, but the majority of seriously restricting the mountain hill areas of economic and social development and the people become rich, of building a moderately prosperous society goals. In the next few years, China will focus on prevention and control area in the flash flood disasters initially built "for the surveillance, communications, forecasting, early warning and other non-engineering measure based, non-engineering measures and the combinations of engineering measures," the mitigation system. The latest progresses on global torrential flood early warning and forecasting techniques are reviewed in this paper, and then an early warning and forecasting approach is proposed on the basis of a distributed hydrological model according to dynamic critical rainfall index. This approach has been applied in Suichuanjiang River basin in Jiangxi province, which is expected to provide valuable reference for building a national flash flood early warning and forecasting system as well as control of such flooding.
In flood forecasting modelling, large basins are often considered as hydrological systems with multiple inputs and one output. Inputs are hydrological variables such rainfall, runoff and physical characteristics of basin; output is runoff. Relating inputs to output can be achieved using deterministic, conceptual, or stochastic models. Rainfall runoff models generally lack of accuracy. Physical hydrological processes based models, either deterministic or conceptual are highly data requirement demanding and by the way very complex. Stochastic multiple input-output models, using only historical chronicles of hydrological variables particularly runoff are by the way very popular among the hydrologists for large river basin flood forecasting. Application is made on the Senegal River upstream of Bakel, where the River is formed by the main branch, Bafing, and two tributaries, Bakoye and Faleme; Bafing being regulated by Manantaly Dam. A three inputs and one output model has been used for flood forecasting on Bakel. Influence of the lead forecasting, and of the three inputs taken separately, then associated two by two, and altogether has been verified using a dimensionless variance as criterion of quality. Inadequacies occur generally between model output and observations; to put model in better compliance with current observations, we have compared four parameter updating procedure, recursive least squares, Kalman filtering, stochastic gradient method, iterative method, and an AR errors forecasting model. A combination of these model updating have been used in real time flood forecasting.(Author)
Full Text Available After a drought during the 1990s, Tlemcen has experienced heavy rainfall in recent years which caused several floods. They have become frequent and usually cause large damage. We then asked ourselves questions about the reasons for this deregulation of rainfall and floods. We have assumed that climate change has led to deregulation of precipitation and that the urbanization and morphology of the site are the causes of the floods. For this, we analyzed the rainfall data and study the configuration of the town of Tlemcen. We noticed then that Tlemcen town undergoes the climate changes effects per a diminution of the multi-annual mean of rainfall between 1974 and 2008, and a slight displacement of the rainfall from April to November after 2008. Finally, the principal reason of floods is the thoughtless urban sprawl on the water courses also favored by an unfavourable topography.
Full Text Available This paper explores the new research challenges for forecasting urban goods demand by rail. In fact, the growing interest to find urban logistics solutions for improving city sustainability and liveability, mainly due to the reduction of urban road accessibility and environmental constraints, has pushed to explore solutions alternative to the road. Multimodal urban logistics, based on the use of railway, seem an interesting alternative solution, but it remained mainly at conceptual level. Few studies have explored the factors, that push actors to find competitive such a system with respect to the road, and modelling framework for forecasting the relative demand. Therefore, paper reviews the current literature, investigates the factors involved in choosing such a mode, and finally, recalls a recent modelling framework and hence proposes some advancements that allow to point out the rail transport alternative.
Hopson, T. M.; Priya, S.; Young, W.; Avasthi, A.; Clayton, T. D.; Brakenridge, G. R.; Birkett, C. M.; Riddle, E. E.; Broman, D.; Boehnert, J.; Sampson, K. M.; Kettner, A.; Singh, D.
During the 2017 South Asia monsoon, torrential rains and catastrophic floods affected more than 45 million people, including 16 million children, across the Ganges-Brahmaputra-Meghna (GBM) basins. The basin is recognized as one of the world's most disaster-prone regions, with severe floods occurring almost annually causing extreme loss of life and property. In light of this vulnerability, the World Bank and collaborators have contributed toward reducing future flood impacts through recent developments to improve operational preparedness for such events, as well as efforts in more general preparedness and resilience building through planning based on detailed risk assessments. With respect to improved event-specific flood preparedness through operational warnings, we discuss a new forecasting system that provides probability-based flood forecasts developed for more than 85 GBM locations. Forecasts are available online, along with near-real-time data maps of rainfall (predicted and actual) and river levels. The new system uses multiple data sets and multiple models to enhance forecasting skill, and provides improved forecasts up to 16 days in advance of the arrival of high waters. These longer lead times provide the opportunity to save both lives and livelihoods. With sufficient advance notice, for example, farmers can harvest a threatened rice crop or move vulnerable livestock to higher ground. Importantly, the forecasts not only predict future water levels but indicate the level of confidence in each forecast. Knowing whether the probability of a danger-level flood is 10 percent or 90 percent helps people to decide what, if any, action to take. With respect to efforts in general preparedness and resilience building, we also present a recent flood risk assessment, and how it provides, for the first time, a numbers-based view of the impacts of different size floods across the Ganges basin. The findings help identify priority areas for tackling flood risks (for
Full Text Available Transport infrastructure networks are increasingly vulnerable to disruption from extreme rainfall events due to increasing surface water runoff from urbanization and changes in climate. Impacts from such disruptions typically extend far beyond the flood footprint, because of the interconnection and spatial extent of modern infrastructure. An integrated flood risk assessment couples high resolution information on depth and velocity from the CityCAT urban flood model with empirical analysis of vehicle speeds in different depths of flood water, to perturb a transport accessibility model and determine the impact of a given event on journey times across the urban area. A case study in Newcastle-upon-Tyne (UK shows that even minor flooding associate with a 1 in 10 year event can cause traffic disruptions of nearly half an hour. Two adaptation scenarios are subsequently tested (i hardening (i.e. flood protection a single major junction, (ii introduction of green roofs across all buildings. Both options have benefits in terms of reduced disruption, but for a 1 in 200 year event greening all roofs in the city provided only three times the benefit of protecting one critical road junction, highlighting the importance of understanding network attributes such as capacity and flows.
Full Text Available Flooding contributes to tremendous hazards every year; more accurate forecasting may significantly mitigate the damages and loss caused by flood disasters. Current hydrological models are either purely knowledge-based or data-driven. A combination of data-driven method (artificial neural networks in this paper and knowledge-based method (traditional hydrological model may booster simulation accuracy. In this study, we proposed a new back-propagation (BP neural network algorithm and applied it in the semi-distributed Xinanjiang (XAJ model. The improved hydrological model is capable of updating the flow forecasting error without losing the leading time. The proposed method was tested in a real case study for both single period corrections and real-time corrections. The results reveal that the proposed method could significantly increase the accuracy of flood forecasting and indicate that the global correction effect is superior to the second-order autoregressive correction method in real-time correction.
Shen, J.-C.; Chang, C. H.; Lien, H. C.; Wu, S. J.; Horng, M. J.
With the global economy and technological development, the degree of urbanization and population density relative to raise. At the same time, a natural buffer space and resources year after year, the situation has been weakened, not only lead to potential environmental disasters, more and more serious, disaster caused by the economy, loss of natural environment at all levels has been expanded. In view of this, the active participation of all countries in the world cross-sectoral integration of disaster prevention technology research and development, in addition, the specialized field of disaster prevention technology, science and technology development, network integration technology, high-speed data transmission and information to support the establishment of mechanisms for disaster management The decision-making and cross-border global disaster information network building and other related technologies, has become the international anti-disaster science and technology development trends, this trend. Naturally a few years in Taiwan, people's lives and property losses caused by many problems related to natural disaster prevention and disaster prevention and the establishment of applications has become a very important. For FEWS_Taiwan, flood warning system developed by the Delft Hydraulics and introduced the Water Resources Agency (WRA), it provides those functionalities for users to modify contents to add the basins, regions, data sources, models and etc. Despite this advantage, version differences due to different users or different teams yet bring about the difficulties on synchronization and integration.At the same time in different research teams will also add different modes of meteorological and hydrological data. From the government perspective of WRA, the need to plan standard operation procedures for system integration demands that the effort for version control due to version differences must be cost down or yet canceled out. As for FEWS_Taiwan, this
Hatim O. Sharif
Full Text Available Riyadh, the capital of the Kingdom of Saudi Arabia, has experienced unusual levels of urbanization in the past few decades, making it one of the fastest growing cities in the world. This paper examines flood hazards in the rapidly urbanizing catchment of Al-Aysen in Riyadh. Remote sensing and geographic information system techniques were employed to obtain and prepare input data for hydrologic and hydraulic models, with the former based on the very popular curve number approach. Due to the limited nature of the rainfall data, observations from two rain gauges in the vicinity of the catchment were used to estimate design storms. The hydrologic model was run in a semi-distributed mode by dividing the catchment into many sub-catchments. The impact of urbanization on run-off volume and peak discharge resulting from different storms was investigated, with various urbanization scenarios simulated. Flood hazard zones and affected streets were also identified through hydrologic/hydraulic model simulation. The mismatch between administrative and catchment boundaries can create problems in flood risk management for similar cities since hydrologic processes and flood hazards are based on the hydrologic connectivity. Since flooding events impact the road network and create driving hazards, governmental decision-makers must take the necessary precautions to protect drivers in these situations.
Gaitan Sabogal, S.; ten Veldhuis, J.A.E.; Rogger, M; Aksoy, H; Kooy, M
Cities worldwide are challenged by increasing urban flood risks. Precise and realistic measures are required to reduce flooding impacts. However, currently implemented sewer and topographic models do not provide realistic predictions of local flooding occurrence during heavy rain events. Assessing
Komac, B; Zorn, M; Natek, K
Damage caused by natural disasters in Slovenia is frequently linked to the ignoring of natural factors in spatial planning. Historically, the construction of buildings and settlements avoided dangerous flood areas, but later we see increasing construction in dangerous areas. During the floods in 1990, the most affected buildings were located on ill-considered locations, and the majority was built in more recent times. A similar situation occurred during the floods of September 2007. Comparing the effects of these floods, we determined that damage was always greater due to the urbanization of flood areas. This process furthermore increasingly limits the 'manoeuvring space' for water management authorities, who due to the torrential nature of Slovenia's rivers can not ensure the required level of safety from flooding for unsuitably located settlements and infrastructure. Every year, the Environmental Agency of the Republic of Slovenia issues more than one thousand permits for interventions in areas that affect the water regime, and through decrees the government allows construction in riparian zones, which is supposedly forbidden by the Law on Water. If we do not take measures with more suitable policies for spatial planning, we will no long have the possibility in future to reduce the negative consequences of floods. Given that torrential floods strike certain Slovene regions every three years on average and that larger floods occur at least once a decade, it is senseless to lay the blame on climate change.
Olsson, J.; Uvo, C. B.; Foster, K.; Yang, W.
Hydropower is a major energy source in Sweden and proper reservoir management prior to the spring flood onset is crucial for optimal production. This requires useful forecasts of the accumulated discharge in the spring flood period (i.e. the spring-flood volume, SFV). Today's SFV forecasts are generated using a model-based climatological ensemble approach, where time series of precipitation and temperature from historical years are used to force a calibrated and initialised set-up of the HBV model. In this study, a number of new approaches to spring flood forecasting, that reflect the latest developments with respect to analysis and modelling on seasonal time scales, are presented and evaluated. Three main approaches, represented by specific methods, are evaluated in SFV hindcasts for three main Swedish rivers over a 10-year period with lead times between 0 and 4 months. In the first approach, historically analogue years with respect to the climate in the period preceding the spring flood are identified and used to compose a reduced ensemble. In the second, seasonal meteorological ensemble forecasts are used to drive the HBV model over the spring flood period. In the third approach, statistical relationships between SFV and the large-sale atmospheric circulation are used to build forecast models. None of the new approaches consistently outperform the climatological ensemble approach, but for specific locations and lead times improvements of 20-30 % are found. When combining all forecasts in a weighted multi-model approach, a mean improvement over all locations and lead times of nearly 10 % was indicated. This demonstrates the potential of the approach and further development and optimisation into an operational system is ongoing.
Large-watershed flood simulation and forecasting is very important for a distributed hydrological model in the application. There are some challenges including the model's spatial resolution effect, model performance and accuracy and so on. To cope with the challenge of the model's spatial resolution effect, different model resolution including 1000m*1000m, 600m*600m, 500m*500m, 400m*400m, 200m*200m were used to build the distributed hydrological model—Liuxihe model respectively. The purpose is to find which one is the best resolution for Liuxihe model in Large-watershed flood simulation and forecasting. This study sets up a physically based distributed hydrological model for flood forecasting of the Liujiang River basin in south China. Terrain data digital elevation model (DEM), soil type and land use type are downloaded from the website freely. The model parameters are optimized by using an improved Particle Swarm Optimization(PSO) algorithm; And parameter optimization could reduce the parameter uncertainty that exists for physically deriving model parameters. The different model resolution (200m*200m—1000m*1000m ) are proposed for modeling the Liujiang River basin flood with the Liuxihe model in this study. The best model's spatial resolution effect for flood simulation and forecasting is 200m*200m.And with the model's spatial resolution reduction, the model performance and accuracy also become worse and worse. When the model resolution is 1000m*1000m, the flood simulation and forecasting result is the worst, also the river channel divided based on this resolution is differs from the actual one. To keep the model with an acceptable performance, minimum model spatial resolution is needed. The suggested threshold model spatial resolution for modeling the Liujiang River basin flood is a 500m*500m grid cell, but the model spatial resolution with a 200m*200m grid cell is recommended in this study to keep the model at a best performance.
Tekeli, Ahmet Emre; Fouli, Hesham
Floods are among the most common disasters harming humanity. In particular, flash floods cause hazards to life, property and any type of structures. Arid and semi-arid regions are equally prone to flash floods like regions with abundant rainfall. Despite rareness of intensive and frequent rainfall events over Kingdom of Saudi Arabia (KSA); an arid/semi-arid region, occasional flash floods occur and result in large amounts of damaging surface runoff. The flooding of 16 November, 2013 in Riyadh; the capital city of KSA, resulted in killing some people and led to much property damage. The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) Real Time (RT) data (3B42RT) are used herein for flash flood forecasting. 3B42RT detected high-intensity rainfall events matching with the distribution of observed floods over KSA. A flood early warning system based on exceedance of threshold limits on 3B42RT data is proposed for Riyadh. Three different indexes: Constant Threshold (CT), Cumulative Distribution Functions (CDF) and Riyadh Flood Precipitation Index (RFPI) are developed using 14-year 3B42RT data from 2000 to 2013. RFPI and CDF with 90% captured the three major flooding events that occurred in February 2005, May 2010 and November 2013 in Riyadh. CT with 3 mm/h intensity indicated the 2013 flooding, but missed those of 2005 and 2010. The methodology implemented herein is a first-step simple and accurate way for flash flood forecasting over Riyadh. The simplicity of the methodology enables its applicability for the TRMM follow-on missions like Global Precipitation Measurement (GPM) mission.
Wang, Y.; Chen, H.; Rosbjerg, Dan
In reservoir operation improvement of the accuracy of forecast flood inflow and extension of forecast lead-time can effectively be achieved by using rainfall forecasts from numerical weather predictions with a hydrological catchment model. In this study, the Regional Spectrum Model (RSM), which...... is developed by the Japan Meteorological Agency, was used to forecast rainfall with 5 days lead-time in the upper region of the Three Gorges Reservoir (TGR). A conceptual hydrological model, the Xinanjiang Model, has been set up to forecast the inflow flood of TGR by the Ministry of Water Resources Information...... season 2012 as example, real-time dynamic control of the FLWL was implemented by using the forecasted reservoir flood inflow as input. The forecasted inflow with 5 days lead-time rainfall forecast was evaluated by several performance indices, including the mean relative error of the volumetric reservoir...
Pillosu, F. M.
Between July and September 2017, the monsoon season caused widespread heavy rainfall and severe floods across countries in South-East Asia, notably in India, Nepal and Bangladesh, with deadly consequences. According to the U.N., in Bangladesh 140 people lost their lives and 700,000 homes were destroyed; in Nepal at least 143 people died, and more than 460,000 people were forced to leave their homes; in India there were 726 victims of flooding and landslides, 3 million people were affected by the monsoon floods and 2000 relief camps were established. Monsoon season happens regularly every year in South Asia, but local authorities reported the last monsoon season as the worst in several years. What made the last monsoon season particularly severe in certain regions? Are these causes clear from the forecasts? Regarding the meteorological characterization of the event, an analysis of forecasts from the European Centre for Medium-Range Weather Forecast (ECMWF) for different lead times (from seasonal to short range) will be shown to evaluate how far in advance this event was predicted and start discussion on what were the factors that led to such a severe event. To illustrate hydrological aspects, forecasts from the Global Flood Awareness System (GloFAS) will be shown. GloFAS is developed at ECMWF in co-operation with the European Commission's Joint Research Centre (JRC) and with the support of national authorities and research institutions such as the University of Reading. It will become operational at the end of 2017 as part of the Copernicus Emergency Management Service. GloFAS couples state-of-the-art weather forecasts with a hydrological model to provide a cross-border system with early flood guidance information to help humanitarian agencies and national hydro-meteorological services to strengthen and improve forecasting capacity, preparedness and mitigation of natural hazards. In this case GloFAS has shown good potential to become a useful tool for better and
Chang, HyungJoon; Lee, Hyosang; Hwang, Myunggyu; Jang, Sukhwan
The changes of land use influence on the flood characteristics, which depend on rainfall runoff procedures in the catchment. This study assesses the changes of flood characteristics due to land use changes between 1997 and 2012. The catchment model (HEC-HMS) is calibrated with flood events of 1990's and 2000's respectively, then the design rainfall of 100, 200, 500year return period are applied to this model, which represent the catchment in 1990's and 2000's, to assess the flood peaks. Then the extreme flood events (i.e., 6 typhoon events) are applied to assess the flood responses. The results of comparison between 1990's and 2000's show that the flood peak and level of 2000's are increasing and time to peak of 2000's is decreasing comparing to those of 1990's :3% to 78% increase in flood peak, 3% in flood level and 10.2% to 16% decrease in time to peak in 100year return period flood. It is due to decreasing of the farmland area (2.18%), mountainous area (8.88%), and increasing of the urbanization of the area (5.86%). This study also estimates the responses to extreme flood events. The results of 2000's show that the increasing of the flood peak and time to peak comparing to 1990's. It indicates that the extreme rainfall is more responsible at unurbanized catchment ( 2000's), which resulting with a 11% increasing of the peak volume. Acknowledgement This research was supported by a grant (11-TI-C06) from Advanced Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.
Li, Ziwei; Liu, Yutong; Cao, Hongjie
In order to monitor the region which disaster flood happened frequently in China, satisfy the great need of province governments for high accuracy monitoring and evaluated data for disaster and improve the efficiency for repelling disaster, under the Ninth Five-year National Key Technologies Programme, the method was researched for flood forecasting and evaluation using satellite and aerial remoted sensed image and land monitor data. The effective and practicable flood forecasting and evaluation system was established and DongTing Lake was selected as the test site. Modern Digital photogrammetry, remote sensing and GIS technology was used in this system, the disastrous flood could be forecasted and loss can be evaluated base on '4D' (DEM -- Digital Elevation Model, DOQ -- Digital OrthophotoQuads, DRG -- Digital Raster Graph, DTI -- Digital Thematic Information) disaster background database. The technology of gathering and establishing method for '4D' disaster environment background database, application technology for flood forecasting and evaluation based on '4D' background data and experimental results for DongTing Lake test site were introduced in detail in this paper.
Liu, Li; Xu, Yue-Ping
Ensemble flood forecasting driven by numerical weather prediction products is becoming more commonly used in operational flood forecasting applications.In this study, a hydrological ensemble flood forecasting system based on Variable Infiltration Capacity (VIC) model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated.The hydrological model is optimized by parallel programmed ɛ-NSGAII multi-objective algorithm and two respectively parameterized models are determined to simulate daily flows and peak flows coupled with a modular approach.The results indicatethat the ɛ-NSGAII algorithm permits more efficient optimization and rational determination on parameter setting.It is demonstrated that the multimodel ensemble streamflow mean have better skills than the best singlemodel ensemble mean (ECMWF) and the multimodel ensembles weighted on members and skill scores outperform other multimodel ensembles. For typical flood event, it is proved that the flood can be predicted 3-4 days in advance, but the flows in rising limb can be captured with only 1-2 days ahead due to the flash feature. With respect to peak flows selected by Peaks Over Threshold approach, the ensemble means from either singlemodel or multimodels are generally underestimated as the extreme values are smoothed out by ensemble process.
Ryu, Young; Lim, Yoon-Jin; Ji, Hee-Sook; Park, Hyun-Hee; Chang, Eun-Chul; Kim, Baek-Jo
In flash flood forecasting, it is necessary to consider not only traditional meteorological variables such as precipitation, evapotranspiration, and soil moisture, but also hydrological components such as streamflow. To address this challenge, the application of high resolution coupled atmospheric-hydrological models is emerging as a promising alternative. This study demonstrates the feasibility of linking a coupled atmospheric-hydrological model (WRF/WRFHydro) with 150-m horizontal grid spacing for flash flood forecasting in Korea. The study area is the Namgang Dam basin in Southern Korea, a mountainous area located downstream of Jiri Mountain (1915 m in height). Under flash flood conditions, the simulated precipitation over the entire basin is comparable to the domain-averaged precipitation, but discharge data from WRF-Hydro shows some differences in the total available water and the temporal distribution of streamflow (given by the timing of the streamflow peak following precipitation), compared to observations. On the basis of sensitivity tests, the parameters controlling the infiltration of excess precipitation and channel roughness depending on stream order are refined and their influence on temporal distribution of streamflow is addressed with intent to apply WRF-Hydro to flash flood forecasting in the Namgang Dam basin. The simulation results from the WRF-Hydro model with optimized parameters demonstrate the potential utility of a coupled atmospheric-hydrological model for forecasting heavy rain-induced flash flooding over the Korean Peninsula.
2Department of Water Engineering, Faculty of Agriculture, Lorestan University, ... decision tree methods for determining flood sus- .... a system to prevent disorder, instability, distur- ..... using support vector machine and GIS at the Golestan.
Full Text Available Although existing hydraulic models have been used to simulate and predict urban flooding, most of these models are inadequate due to the high spatial resolution required to simulate flows in urban floodplains. Nesting high-resolution subdomains within coarser-resolution models is an efficient solution for enabling simultaneous calculation of flooding due to tides, surges, and high river flows. MSN_Flood has been developed to incorporate moving boundaries around nested domains, permitting alternate flooding and drying along the boundary and in the interior of the domain. Ghost cells adjacent to open boundary cells convert open boundaries, in effect, into internal boundaries. The moving boundary may be multi-segmented and non-continuous, with recirculating flow across the boundary. When combined with a bespoke adaptive interpolation scheme, this approach facilitates a dynamic internal boundary. Based on an alternating-direction semi-implicit finite difference scheme, MSN_Flood was used to hindcast a major flood event in Cork City resulting from the combined pressures of fluvial, tidal, and storm surge processes. The results show that the model is computationally efficient, as the 2-m high-resolution nest is used only in the urban flooded region. Elsewhere, lower-resolution nests are used. The results also show that the model is highly accurate when compared with measured data. The model is capable of incorporating nested sub-domains when the nested boundary is multi-segmented and highly complex with lateral gradients of elevation and velocities. This is a major benefit when modelling urban floodplains at very high resolution.
Taylor, Jonathon; Lai, Ka Man; Davies, Mike; Clifton, David; Ridley, Ian; Biddulph, Phillip
, insurance companies and residents to better understand, prepare for and manage a large-scale flood in urban environments. Copyright © 2011 Elsevier Ltd. All rights reserved.
Rogelis Prada, M.C.
Flood early warning systems are recognized as one of the most effective flood risk management instruments when correctly embedded in comprehensive flood risk management strategies and policies. Many efforts around the world are being put in place to advance the components that determine the
Krøgli, Ingeborg; Fleig, Anne; Glad, Per; Dahl, Mads-Peter; Devoli, Graziella; Colleuille, Hervé
While flood forecasting at national level is quite well established and operational in many countries worldwide, landslide forecasting at national level is still seldom. Examples of coordinated flood and landslide forecasting are even rarer. Most of the time flood and landslide forecasters work separately (investigating, defining thresholds, and developing models) and most of the time without communication with each other. One example of coordinated operational early warning systems (EWS) for flooding and shallow landslides is found at the Norwegian Water Resources and Energy Directorate (NVE) in Norway. In this presentation we give an introduction to the two separate but tightly collaborative EWSs and to the coordination of these. The two EWSs are being operated from the same office, every day using similar hydro-meteorological prognosis and hydrological models. Prognosis and model outputs on e.g. discharge, snow melt, soil water content and exceeded landslide thresholds are evaluated in a web based decision-making tool (xgeo.no). The experts performing forecasts are hydrologists, geologists and physical geographers. A similar warning scale, based on colors (green, yellow, orange and red) is used for both EWSs, however thresholds for flood and landslide warning levels are defined differently. Also warning areas may not necessary be the same for both hazards and depending on the specific meteorological event, duration of the warning periods can differ. We present how knowledge, models and tools, but also human and economic resources are being shared between the two EWSs. Moreover, we discuss challenges faced in the communication of warning messages using recent flood and landslide events as examples.
Yu, Wansik; Nakakita, Eiichi; Kim, Sunmin; Yamaguchi, Kosei
The use of meteorological ensembles to produce sets of hydrological predictions increased the capability to issue flood warnings. However, space scale of the hydrological domain is still much finer than meteorological model, and NWP models have challenges with displacement. The main objective of this study to enhance the transposition method proposed in Yu et al. (2014) and to suggest the post-processing ensemble flood forecasting method for the real-time updating and the accuracy improvement of flood forecasts that considers the separation of the orographic rainfall and the correction of misplaced rain distributions using additional ensemble information through the transposition of rain distributions. In the first step of the proposed method, ensemble forecast rainfalls from a numerical weather prediction (NWP) model are separated into orographic and non-orographic rainfall fields using atmospheric variables and the extraction of topographic effect. Then the non-orographic rainfall fields are examined by the transposition scheme to produce additional ensemble information and new ensemble NWP rainfall fields are calculated by recombining the transposition results of non-orographic rain fields with separated orographic rainfall fields for a generation of place-corrected ensemble information. Then, the additional ensemble information is applied into a hydrologic model for post-flood forecasting with a 6-h interval. The newly proposed method has a clear advantage to improve the accuracy of mean value of ensemble flood forecasting. Our study is carried out and verified using the largest flood event by typhoon 'Talas' of 2011 over the two catchments, which are Futatsuno (356.1 km2) and Nanairo (182.1 km2) dam catchments of Shingu river basin (2360 km2), which is located in the Kii peninsula, Japan.
Sulafa Hag Elsafi
Full Text Available Heavy seasonal rains cause the River Nile in Sudan to overflow and flood the surroundings areas. The floods destroy houses, crops, roads, and basic infrastructure, resulting in the displacement of people. This study aimed to forecast the River Nile flow at Dongola Station in Sudan using an Artificial Neural Network (ANN as a modeling tool and validated the accuracy of the model against actual flow. The ANN model was formulated to simulate flows at a certain location in the river reach, based on flow at upstream locations. Different procedures were applied to predict flooding by the ANN. Readings from stations along the Blue Nile, White Nile, Main Nile, and River Atbara between 1965 and 2003 were used to predict the likelihood of flooding at Dongola Station. The analysis indicated that the ANN provides a reliable means of detecting the flood hazard in the River Nile.
Justin A. Schulte
Full Text Available In this paper, it is proposed that coastal flood ensemble forecasts be partitioned into sub-ensemble forecasts using cluster analysis in order to produce representative statistics and to measure forecast uncertainty arising from the presence of clusters. After clustering the ensemble members, the ability to predict the cluster into which the observation will fall can be measured using a cluster skill score. Additional sub-ensemble and composite skill scores are proposed for assessing the forecast skill of a clustered ensemble forecast. A recently proposed method for statistically increasing the number of ensemble members is used to improve sub-ensemble probabilistic estimates. Through the application of the proposed methodology to Sandy coastal flood reforecasts, it is demonstrated that statistics computed using only ensemble members belonging to a specific cluster are more representative than those computed using all ensemble members simultaneously. A cluster skill-cluster uncertainty index relationship is identified, which is the cluster analog of the documented spread-skill relationship. Two sub-ensemble skill scores are shown to be positively correlated with cluster forecast skill, suggesting that skillfully forecasting the cluster into which the observation will fall is important to overall forecast skill. The identified relationships also suggest that the number of ensemble members within in each cluster can be used as guidance for assessing the potential for forecast error. The inevitable existence of ensemble member clusters in tidally dominated total water level prediction systems suggests that clustering is a necessary post-processing step for producing representative and skillful total water level forecasts.
Dugar, Sumit; Smith, Paul; Parajuli, Binod; Khanal, Sonu; Brown, Sarah; Gautam, Dilip; Bhandari, Dinanath; Gurung, Gehendra; Shakya, Puja; Kharbuja, RamGopal; Uprety, Madhab
Operationalising effective Flood Early Warning Systems (EWS) in developing countries like Nepal poses numerous challenges, with complex topography and geology, sparse network of river and rainfall gauging stations and diverse socio-economic conditions. Despite these challenges, simple real-time monitoring based EWSs have been in place for the past decade. A key constraint of these simple systems is the very limited lead time for response - as little as 2-3 hours, especially for rivers originating from steep mountainous catchments. Efforts to increase lead time for early warning are focusing on imbedding forecasts into the existing early warning systems. In 2016, the Nepal Department of Hydrology and Meteorology (DHM) piloted an operational Probabilistic Flood Forecasting Model in major river basins across Nepal. This comprised a low data approach to forecast water levels, developed jointly through a research/practitioner partnership with Lancaster University and WaterNumbers (UK) and the International NGO Practical Action. Using Data-Based Mechanistic Modelling (DBM) techniques, the model assimilated rainfall and water levels to generate localised hourly flood predictions, which are presented as probabilistic forecasts, increasing lead times from 2-3 hours to 7-8 hours. The Nepal DHM has simultaneously started utilizing forecasts from the Global Flood Awareness System (GLoFAS) that provides streamflow predictions at the global scale based upon distributed hydrological simulations using numerical ensemble weather forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts). The aforementioned global and local models have already affected the approach to early warning in Nepal, being operational during the 2016 monsoon in the West Rapti basin in Western Nepal. On 24 July 2016, GLoFAS hydrological forecasts for the West Rapti indicated a sharp rise in river discharge above 1500 m3/sec (equivalent to the river warning level at 5 meters) with 53
Arnal, Louise; Pappenberger, Florian; Ramos, Maria-Helena; Cloke, Hannah; Crochemore, Louise; Giuliani, Matteo; Aalbers, Emma
In the design and building of an operational flood forecasting system, a large number of decisions have to be taken. These include technical decisions related to the choice of the meteorological forecasts to be used as input to the hydrological model, the choice of the hydrological model itself (its structure and parameters), the selection of a data assimilation procedure to run in real-time, the use (or not) of a post-processor, and the computing environment to run the models and display the outputs. Additionally, a number of trans-disciplinary decisions are also involved in the process, such as the way the needs of the users will be considered in the modelling setup and how the forecasts (and their quality) will be efficiently communicated to ensure usefulness and build confidence in the forecasting system. We propose to reflect on the numerous, alternative pathways to designing and running an operational flood forecasting system through an adventure game. In this game, the player is the protagonist of an interactive story driven by challenges, exploration and problem-solving. For this presentation, you will have a chance to play this game, acting as the leader of a forecasting team at an operational centre. Your role is to manage the actions of your team and make sequential decisions that impact the design and running of the system in preparation to and during a flood event, and that deal with the consequences of the forecasts issued. Your actions are evaluated by how much they cost you in time, money and credibility. Your aim is to take decisions that will ultimately lead to a good balance between time and money spent, while keeping your credibility high over the whole process. This game was designed to highlight the complexities behind decision-making in an operational forecasting and emergency response context, in terms of the variety of pathways that can be selected as well as the timescale, cost and timing of effective actions.
Rakovec, O.; Weerts, A. H.; Sumihar, J.; Uijlenhoet, R.
This study investigates the suitability of the asynchronous ensemble Kalman filter (AEnKF) and a partitioned updating scheme for hydrological forecasting. The AEnKF requires forward integration of the model for the analysis and enables assimilation of current and past observations simultaneously at a single analysis step. The results of discharge assimilation into a grid-based hydrological model (using a soil moisture error model) for the Upper Ourthe catchment in the Belgian Ardennes show that including past predictions and observations in the data assimilation method improves the model forecasts. Additionally, we show that elimination of the strongly non-linear relation between the soil moisture storage and assimilated discharge observations from the model update becomes beneficial for improved operational forecasting, which is evaluated using several validation measures.
The effects of climate change and increasing urbanisation call for a new paradigm for efficient planning, management and retrofitting of urban developments to increase resilience to climate change and to maximize ecosystem services. Improved management of urban floods from all sources in required. Time scale for well documented fluvial and coastal floods allows for timely response but surface (pluvial) flooding caused by intense local storms had not been given appropriate attention, Pitt Review (UK). Urban surface floods predictions require fine scale data and model resolutions. They have to be tackled locally by combining central inputs (meteorological services) with the efforts of the local entities. Although significant breakthrough in modelling of pluvial flooding was made there is a need to further enhance short term prediction of both rainfall and surface flooding. These issues are dealt with in the EU Iterreg project Rain Gain (RG). Breakthrough in urban flood mitigation can only be achieved by combined effects of advanced planning design, construction and management of urban water (blue) assets in interaction with urban vegetated areas' (green) assets. Changes in design and operation of blue and green assets, currently operating as two separate systems, is urgently required. Gaps in knowledge and technology will be introduced by EIT's Climate-KIC Blue Green Dream (BGD) project. The RG and BGD projects provide synergy of the "decoupled" blue and green systems to enhance multiple benefits to: urban amenity, flood management, heat island, biodiversity, resilience to drought thus energy requirements, thus increased quality of urban life at lower costs. Urban pluvial flood management will address two priority areas: Short Term rainfall Forecast and Short term flood surface forecast. Spatial resolution of short term rainfall forecast below 0.5 km2 and lead time of a few hours are needed. Improvements are achievable by combining data sources of raingauge networks
Modeling flash flood events in arid environments is a difficult but important task that has impacts on both water resource related issues and also emergency management and response. The challenge is often related to adequately describing the precursor intense rainfall events that cause these flood responses, as they are generally poorly simulated and forecast. Jeddah, the second largest city in the Kingdom of Saudi Arabia, has suffered from a number of flash floods over the last decade, following short-intense rainfall events. The research presented here focuses on examining four historic Jeddah flash floods (Nov. 25-26 2009, Dec. 29-30 2010, Jan. 14-15 2011 and Jan. 25-26 2011) and investigates the feasibility of using numerical weather prediction models to achieve a more realistic simulation of these flood-producing rainfall events. The Weather Research and Forecasting (WRF) model (version 3.5) is used to simulate precipitation and meteorological conditions via a high-resolution inner domain (1-km) around Jeddah. A range of different convective closure and microphysics parameterization, together with high-resolution (4-km) sea surface temperature data are employed. Through examining comparisons between the WRF model output and in-situ, radar and satellite data, the characteristics and mechanism producing the extreme rainfall events are discussed and the capacity of the WRF model to accurately forecast these rainstorms is evaluated.
Deng, Liping; McCabe, Matthew; Stenchikov, Georgiy L.; Evans, Jason; Kucera, Paul
Modeling flash flood events in arid environments is a difficult but important task that has impacts on both water resource related issues and also emergency management and response. The challenge is often related to adequately describing the precursor intense rainfall events that cause these flood responses, as they are generally poorly simulated and forecast. Jeddah, the second largest city in the Kingdom of Saudi Arabia, has suffered from a number of flash floods over the last decade, following short-intense rainfall events. The research presented here focuses on examining four historic Jeddah flash floods (Nov. 25-26 2009, Dec. 29-30 2010, Jan. 14-15 2011 and Jan. 25-26 2011) and investigates the feasibility of using numerical weather prediction models to achieve a more realistic simulation of these flood-producing rainfall events. The Weather Research and Forecasting (WRF) model (version 3.5) is used to simulate precipitation and meteorological conditions via a high-resolution inner domain (1-km) around Jeddah. A range of different convective closure and microphysics parameterization, together with high-resolution (4-km) sea surface temperature data are employed. Through examining comparisons between the WRF model output and in-situ, radar and satellite data, the characteristics and mechanism producing the extreme rainfall events are discussed and the capacity of the WRF model to accurately forecast these rainstorms is evaluated.
Bach, H.; Appel, F.; Schulz, W.; Merkel, U.; Ludwig, R.; Mauser, W.
Methods to accurately assess and forecast flood discharge are mandatory to minimise the impact of hydrological hazards. However, existing rainfall-runoff models rarely accurately consider the spatial characteristics of the watershed, which is essential for a suitable and physics-based description of processes relevant for runoff formation. Spatial information with low temporal variability like elevation, slopes and land use can be mapped or extracted from remote sensing data. However, land surface param- eters of high temporal variability, like soil moisture and snow properties are hardly available and used in operational forecasts. Remote sensing methods can improve flood forecast by providing information on the actual water retention capacities in the watershed and facilitate the regionalisation of hydrological models. To prove and demonstrate this, the project 'InFerno' (Integration of remote sensing data in opera- tional water balance and flood forecast modelling) has been set up, funded by DLR (50EE0053). Within InFerno remote sensing data (optical and microwave) are thor- oughly processed to deliver spatially distributed parameters of snow properties and soil moisture. Especially during the onset of a flood this information is essential to estimate the initial conditions of the model. At the flood forecast centres of 'Baden- Württemberg' and 'Rheinland-Pfalz' (Southwest Germany) the remote sensing based maps on soil moisture and snow properties will be integrated in the continuously op- erated water balance and flood forecast model LARSIM. The concept is to transfer the developed methodology from the Neckar to the Mosel basin. The major challenges lie on the one hand in the implementation of algorithms developed for a multisensoral synergy and the creation of robust, operationally applicable remote sensing products. On the other hand, the operational flood forecast must be adapted to make full use of the new data sources. In the operational phase of the
Li, Ji; Chen, Yangbo; Wang, Huanyu; Qin, Jianming; Li, Jie; Chiao, Sen
Long lead time flood forecasting is very important for large watershed flood mitigation as it provides more time for flood warning and emergency responses. The latest numerical weather forecast model could provide 1-15-day quantitative precipitation forecasting products in grid format, and by coupling this product with a distributed hydrological model could produce long lead time watershed flood forecasting products. This paper studied the feasibility of coupling the Liuxihe model with the Weather Research and Forecasting quantitative precipitation forecast (WRF QPF) for large watershed flood forecasting in southern China. The QPF of WRF products has three lead times, including 24, 48 and 72 h, with the grid resolution being 20 km × 20 km. The Liuxihe model is set up with freely downloaded terrain property; the model parameters were previously optimized with rain gauge observed precipitation, and re-optimized with the WRF QPF. Results show that the WRF QPF has bias with the rain gauge precipitation, and a post-processing method is proposed to post-process the WRF QPF products, which improves the flood forecasting capability. With model parameter re-optimization, the model's performance improves also. This suggests that the model parameters be optimized with QPF, not the rain gauge precipitation. With the increasing of lead time, the accuracy of the WRF QPF decreases, as does the flood forecasting capability. Flood forecasting products produced by coupling the Liuxihe model with the WRF QPF provide a good reference for large watershed flood warning due to its long lead time and rational results.
Full Text Available The presented paper aims to analyze the influence of the selection of transfer function and training algorithms on neural network flood runoff forecast. Nine of the most significant flood events, caused by the extreme rainfall, were selected from 10 years of measurement on small headwater catchment in the Czech Republic, and flood runoff forecast was investigated using the extensive set of multilayer perceptrons with one hidden layer of neurons. The analyzed artificial neural network models with 11 different activation functions in hidden layer were trained using 7 local optimization algorithms. The results show that the Levenberg-Marquardt algorithm was superior compared to the remaining tested local optimization methods. When comparing the 11 nonlinear transfer functions, used in hidden layer neurons, the RootSig function was superior compared to the rest of analyzed activation functions.
Amadio, P.; Mancini, M.; Mazzetti, P.; Menduni, G.; Nativi, S.; Rabuffetti, D.; Ravazzani, G.; Rosso, R.
The pluviometric flood forecasting thresholds are an easy method that helps river flood emergency management collecting data from limited area meteorologic model or telemetric raingauges. The thresholds represent the cumulated rainfall depth which generate critic discharge for a particular section. The thresholds were calculated for different sections of Arno river and for different antecedent moisture condition using the flood event distributed hydrologic model FEST. The model inputs were syntethic hietographs with different shape and duration. The system realibility has been verified by generating 500 year syntethic rainfall for 3 important subwatersheds of the studied area. A new technique to consider spatial variability of rainfall and soil properties effects on hydrograph has been investigated. The "Geomorphologic Weights" were so calculated. The alarm system has been implemented in a dedicated software (MIMI) that gets measured and forecast rainfall data from Autorità di Bacino and defines the state of the alert of the river sections.
Full Text Available Accurate and timely flash floods forecasting, especially, in ungauged and poorly gauged basins, is one of the most important and challenging problems to be solved by the international hydrological community. In changing climate and variable anthropogenic impact on river basins, as well as due to low density of surface hydrometeorological network, flash flood forecasting based on “traditional” physically based, or conceptual, or statistical hydrological models often becomes inefficient. Unfortunately, most of river basins in Russia are poorly gauged or ungauged; besides, lack of hydrogeological data is quite typical. However, the developing economy and population safety necessitate issuing warnings based on reliable forecasts. For this purpose, a new hydrological model, MLCM3 (Multi-Layer Conceptual Model, 3 rd generation has been developed in the Russian State Hydrometeorological University. The model showed good results in more than 50 tested basins.
Full Text Available Real-time Model Predictive Control (MPC of hydraulic structures strongly reduces flood consequences under ideal circumstances. The performance of such flood control may, however, be significantly affected by uncertainties. This research quantifies the influence of rainfall forecast uncertainties and related uncertainties in the catchment rainfall-runoff discharges on the control performance for the Herk river case study in Belgium. To limit the model computational times, a fast conceptual model is applied. It is calibrated to a full hydrodynamic river model. A Reduced Genetic Algorithm is used as optimization method. Next to the analysis of the impact of the rainfall forecast uncertainties on the control performance, a Multiple Model Predictive Control (MMPC approach is tested to reduce this impact. Results show that the deterministic MPC-RGA outperforms the MMPC and that it is inherently robust against rainfall forecast uncertainties due to its receding horizon strategy.
Jones, M.; Longenecker, H. E., III
The 2017 hurricane season brought the unprecedented landfall of three Category 4 hurricanes (Harvey, Irma and Maria). FEMA is responsible for coordinating the federal response and recovery efforts for large disasters such as these. FEMA depends on timely and accurate depth grids to estimate hazard exposure, model damage assessments, plan flight paths for imagery acquisition, and prioritize response efforts. In order to produce riverine or coastal depth grids based on observed flooding, the methodology requires peak crest water levels at stream gauges, tide gauges, high water marks, and best-available elevation data. Because peak crest data isn't available until the apex of a flooding event and high water marks may take up to several weeks for field teams to collect for a large-scale flooding event, final observed depth grids are not available to FEMA until several days after a flood has begun to subside. Within the last decade NOAA's National Weather Service (NWS) has implemented the Advanced Hydrologic Prediction Service (AHPS), a web-based suite of accurate forecast products that provide hydrograph forecasts at over 3,500 stream gauge locations across the United States. These forecasts have been newly implemented into an automated depth grid script tool, using predicted instead of observed water levels, allowing FEMA access to flood hazard information up to 3 days prior to a flooding event. Water depths are calculated from the AHPS predicted flood stages and are interpolated at 100m spacing along NHD hydrolines within the basin of interest. A water surface elevation raster is generated from these water depths using an Inverse Distance Weighted interpolation. Then, elevation (USGS NED 30m) is subtracted from the water surface elevation raster so that the remaining values represent the depth of predicted flooding above the ground surface. This automated process requires minimal user input and produced forecasted depth grids that were comparable to post
Murla Tuyls, Damian; Thorndahl, Søren
Intense rainfall can often cause severe floods, especially in urbanized areas, where population density or large impermeable areas are found. In this context, floods can generate a direct impact in a social-environmental-economic viewpoint. Traditionally, in design of Urban Drainage Systems (UDS), correlation between return period (RP) of a given rainfall and RP of its consequent flood has been assumed to be linear (e.g. DS/EN752 (2008)). However, this is not always the case. Complex UDS, where diverse hydraulic infrastructures are often found, increase the heterogeneity of system response, which may cause an alteration of the mentioned correlation. Consequently, reliability on future urban planning, design and resilience against floods may be also affected by this misassumption. In this study, an assessment of surface flood RP across rainfall RP has been carried out at Lystrup, a urbanized catchment area of 440ha and 10.400inhab. located in Jutland (Denmark), which has received the impact of several pluvial flooding in the last recent years. A historical rainfall dataset from the last 35 years from two different rain gauges located at 2 and 10 km from the study area has been provided by the Danish Wastewater Pollution Committee and the Danish Meteorological Institute (DMI). The most extreme 25 rainfall events have been selected through a two-step multi-criteria procedure, ensuring an adequate variability of rainfall, from extreme high peak storms with a short duration to moderate rainfall with longer duration. In addition, a coupled 1D/2D surface and network UDS model of the catchment area developed in an integrated MIKE URBAN and MIKE Flood model (DHI 2014), considering both permeable and impermeable areas, in combination with a DTM (2x2m res.) has been used to study and assess in detail flood RP. Results show an ambiguous relation between rainfall RP and flood response. Local flood levels, flood area and volume RP estimates should therefore not be neglected in
. It has been estimated that the total amount lost was approximately R3 000 million. South African farmers lost more than 50% of their export products. Flood damages and disruptions to humans and animal species were even bigger in ...
De Kleermaeker, S.; Verlaan, M.; Kroos, J.; Zijl, F.
The North Sea is one of the busiest seas in the world with dense ship traffic, fisheries, wind farming, recreation and many other activities. All these activities depend on the ‘marine weather’. Accurate forecasts of waves, currents and sea level are crucial for operational management and for
The significance of the introduction of modern computer technology to design, analysis, operation and control of sewer systems during rain is analyzed. With new tools come new basic concepts and new engineering criteria for performance. The most significant developments are: the capability...... to calculate surcharging and flooding, rather than just relating to pipe capacity performance criteria; the capability of calculating long series of rain record in order to derive proper statistics on the pollutional load on the environment; and finally the capability of dynamically controlling the system...
Stanzel, Ph; Kahl, B; Haberl, U; Herrnegger, M; Nachtnebel, H P
A hydrological modelling framework applied within operational flood forecasting systems in three alpine Danube tributary basins, Traisen, Salzach and Enns, is presented. A continuous, semi-distributed rainfall-runoff model, accounting for the main hydrological processes of snow accumulation and melt, interception, evapotranspiration, infiltration, runoff generation and routing is set up. Spatial discretization relies on the division of watersheds into subbasins and subsequently into hydrologic response units based on spatial information on soil types, land cover and elevation bands. The hydrological models are calibrated with meteorological ground measurements and with meteorological analyses incorporating radar information. Operationally, each forecasting sequence starts with the re-calculation of the last 24 to 48 hours. Errors between simulated and observed runoff are minimized by optimizing a correction factor for the input to provide improved system states. For the hydrological forecast quantitative 48 or 72 hour forecast grids of temperature and precipitation - deterministic and probabilistic - are used as input. The forecasted hydrograph is corrected with an autoregressive model. The forecasting sequences are repeated each 15 minutes. First evaluations of resulting hydrological forecasts are presented and reliability of forecasts with different lead times is discussed.
Full Text Available River cities require a management approach based on resilience to floods rather than on resistance. Resisting floods by means of levees, dams, and channelization neglects inherent uncertainties arising from human-nature couplings and fails to address the extreme events that are expected to increase with climate change, and is thereby not a reliable approach to long-term flood safety. By applying resilience theory to address system persistence through changes, I develop a theory on "urban resilience to floods" as an alternative framework for urban flood hazard management. Urban resilience to floods is defined as a city's capacity to tolerate flooding and to reorganize should physical damage and socioeconomic disruption occur, so as to prevent deaths and injuries and maintain current socioeconomic identity. It derives from living with periodic floods as learning opportunities to prepare the city for extreme ones. The theory of urban resilience to floods challenges the conventional wisdom that cities cannot live without flood control, which in effect erodes resilience. To operationalize the theory for planning practice, a surrogate measure - the percent floodable area - is developed for assessing urban resilience to floods. To enable natural floodplain functions to build urban resilience to floods, flood adaptation is advocated in order to replace flood control for mitigating flood hazards.
Berthet, Lionel; Marty, Renaud; Bourgin, François; Viatgé, Julie; Piotte, Olivier; Perrin, Charles
An increasing number of operational flood forecasting centres assess the predictive uncertainty associated with their forecasts and communicate it to the end users. This information can match the end-users needs (i.e. prove to be useful for an efficient crisis management) only if it is reliable: reliability is therefore a key quality for operational flood forecasts. In 2015, the French flood forecasting national and regional services (Vigicrues network; www.vigicrues.gouv.fr) implemented a framework to compute quantitative discharge and water level forecasts and to assess the predictive uncertainty. Among the possible technical options to achieve this goal, a statistical analysis of past forecasting errors of deterministic models has been selected (QUOIQUE method, Bourgin, 2014). It is a data-based and non-parametric approach based on as few assumptions as possible about the forecasting error mathematical structure. In particular, a very simple assumption is made regarding the predictive uncertainty distributions for large events outside the range of the calibration data: the multiplicative error distribution is assumed to be constant, whatever the magnitude of the flood. Indeed, the predictive distributions may not be reliable in extrapolation. However, estimating the predictive uncertainty for these rare events is crucial when major floods are of concern. In order to improve the forecasts reliability for major floods, an attempt at combining the operational strength of the empirical statistical analysis and a simple error modelling is done. Since the heteroscedasticity of forecast errors can considerably weaken the predictive reliability for large floods, this error modelling is based on the log-sinh transformation which proved to reduce significantly the heteroscedasticity of the transformed error in a simulation context, even for flood peaks (Wang et al., 2012). Exploratory tests on some operational forecasts issued during the recent floods experienced in
Hussain, M.A.; Abbas, A.; Ansari, M.R.K.
The climatic signature of global warming is both local and global. The forcing by increasing greenhouse gases is global, so there is clearly a global component to the climatic signature. Moreover, the damaging impacts of global warming are manifesting themselves around the world in the form of extreme weather events like storms, tornadoes, floods and droughts, all of which have been escalating in frequency and intensity. Furthermore, it is a well-known fact that there is high degree of uncertainty surrounding projections of basic climate variables, such as temperature and precipitation. However, numerous authors have explored many of these effects individually and have begun exploring the interactions between climate change-induced impacts in different sectors of urban activities. Therefore, it is safe to say that an attempt to conduct a definitive, comprehensive analysis of all the potential impacts of climate change on the urban structure is premature at present. This communication attempts to examine the trends in maximum monthly urban temperature fluctuations. Analysis reveals increasing trends in urban temperature fluctuations showing effect of Karachi industrializations. Forecast models also suggest future scenario with respect to occurrence of extreme temperature. The analysis carried out in this work would be useful for urban planners for sustainable future development, economists and environmentalists etc. (author)
Mark, Ole; Jørgensen, Claus; Hammond, Michael
outlines a novel methodology for linking dynamic urban flood modelling with quantitative microbial risk assessment (QMRA). This provides a unique possibility for understanding the interaction between urban flooding and health risk caused by direct human contact with the flood water and hence gives...... and mortality, especially during floods. At present, there are no software tools capable of combining hydrodynamic modelling and health risk analyses, and the links between urban flooding and the health risk for the population due to direct contact with the flood water are poorly understood. The present paper...... an option for reducing the burden of disease in the population by use of intelligent urban flood risk management. The model linking urban flooding and health risk is applied to Dhaka City in Bangladesh, where waterborne diseases including cholera are endemic. The application to Dhaka City is supported...
Pan, Luying; Chen, Yangbo; Zhang, Tao
Shigu creek is a highly urbanized small watershed in Dongguan City. Due to rapid urbanization, quick flood response has been observed, which posted great threat to the flood security of Dongguan City. To evaluate the impact of urbanization on the flood changes of Shigu creek is very important for the flood mitigation of Shigu creek, which will provide insight for flood planners and managers for if to build a larger flood mitigation system. In this paper, the Land cover/use changes of Shigu creek from 1987-2015 induced by urbanization was first extracted from a local database, then, the Liuxihe model, a physically based distributed hydrological model, is employed to simulate the flood processes impacted by urbanization. Precipitation of 3 storms was used for flood processes simulation. The results show that the runoff coefficient and peak flow have increased sharply.
van der Zwan, Rene
The Rijnland water system is situated in the western part of the Netherlands, and is a low-lying area of which 90% is below sea-level. The area covers 1,100 square kilometres, where 1.3 million people live, work, travel and enjoy leisure. The District Water Control Board of Rijnland is responsible for flood defence, water quantity and quality management. This includes design and maintenance of flood defence structures, control of regulating structures for an adequate water level management, and waste water treatment. For water quantity management Rijnland uses, besides an online monitoring network for collecting water level and precipitation data, a real time control decision support system. This decision support system consists of deterministic hydro-meteorological forecasts with a 24-hr forecast horizon, coupled with a control module that provides optimal operation schedules for the storage basin pumping stations. The uncertainty of the rainfall forecast is not forwarded in the hydrological prediction. At this moment 65% of the pumping capacity of the storage basin pumping stations can be automatically controlled by the decision control system. Within 5 years, after renovation of two other pumping stations, the total capacity of 200 m3/s will be automatically controlled. In critical conditions there is a need of both a longer forecast horizon and a probabilistic forecast. Therefore ensemble precipitation forecasts of the ECMWF are already consulted off-line during dry-spells, and Rijnland is running a pilot operational system providing 10-day water level ensemble forecasts. The use of EPS during dry-spells and the findings of the pilot will be presented. Challenges and next steps towards on-line implementation of ensemble forecasts for risk-based operational management of the Rijnland water system will be discussed. An important element in that discussion is the question: will policy and decision makers, operator and citizens adapt this Anticipatory Water
that combines a model for the socio-economic development of cities (DANCE4WATER) with an urban flood model. The urban flood model is a 1D-2D spatially distributed hydrologic and hydraulic model that, for a given urban layout, simulates flow in the sewer system and the surface flow in the catchment (MIKE FLOOD......). The socio-economic model computes urban layouts that are transferred to the hydraulic model in the form of changes of impervious area and potential flow paths on the surface. Estimates of flood prone areas, as well as the expected annual damage due to flooding, are returned to the socio-economic model...... as an input for further refinement of the scenarios for the urban development. Our results in an Australian case study suggest that urban development is a major driver for flood risk and vice versa that flood risk can be significantly reduced if it is accounted for in the development of the cities...
Chou, Po-Kai; Chang, Li-Chiu; Chang, Fi-John; Shih, Ban-Jwu
Because mountainous terrains and steep landforms rapidly accelerate the speed of flood flow in Taiwan island, accurate multi-step-ahead inflow forecasts during typhoon events for providing reliable information benefiting the decision-makings of reservoir pre-storm release and flood-control operation are considered crucial and challenging. Various types of artificial neural networks (ANNs) have been successfully applied in hydrological fields. This study proposes a recurrent configuration of the nonlinear autoregressive with exogenous inputs (NARX) network, called R-NARX, with various effective inputs to forecast the inflows of the Feitsui Reservoir, a pivot reservoir for water supply to Taipei metropolitan in Taiwan, during typhoon periods. The proposed R-NARX is constructed based on the recurrent neural network (RNN), which is commonly used for modelling nonlinear dynamical systems. A large number of hourly rainfall and inflow data sets collected from 95 historical typhoon events in the last thirty years are used to train, validate and test the models. The potential input variables, including rainfall in previous time steps (one to six hours), cumulative rainfall, the storage factor and the storage function, are assessed, and various models are constructed with their reliability and accuracy being tested. We find that the previous (t-2) rainfall and cumulative rainfall are crucial inputs and the storage factor and the storage function would also improve the forecast accuracy of the models. We demonstrate that the R-NARX model not only can accurately forecast the inflows but also effectively catch the peak flow without adopting observed inflow data during the entire typhoon period. Besides, the model with the storage factor is superior to the model with the storage function, where its improvement can reach 24%. This approach can well model the rainfall-runoff process for the entire flood forecasting period without the use of observed inflow data and can provide
J. C. Bartholmes
Full Text Available Since 2005 the European Flood Alert System (EFAS has been producing probabilistic hydrological forecasts in pre-operational mode at the Joint Research Centre (JRC of the European Commission. EFAS aims at increasing preparedness for floods in trans-national European river basins by providing medium-range deterministic and probabilistic flood forecasting information, from 3 to 10 days in advance, to national hydro-meteorological services.
This paper is Part 2 of a study presenting the development and skill assessment of EFAS. In Part 1, the scientific approach adopted in the development of the system has been presented, as well as its basic principles and forecast products. In the present article, two years of existing operational EFAS forecasts are statistically assessed and the skill of EFAS forecasts is analysed with several skill scores. The analysis is based on the comparison of threshold exceedances between proxy-observed and forecasted discharges. Skill is assessed both with and without taking into account the persistence of the forecasted signal during consecutive forecasts.
Skill assessment approaches are mostly adopted from meteorology and the analysis also compares probabilistic and deterministic aspects of EFAS. Furthermore, the utility of different skill scores is discussed and their strengths and shortcomings illustrated. The analysis shows the benefit of incorporating past forecasts in the probability analysis, for medium-range forecasts, which effectively increases the skill of the forecasts.
Matte, Simon; Boucher, Marie-Amélie; Boucher, Vincent; Fortier-Filion, Thomas-Charles
A large effort has been made over the past 10 years to promote the operational use of probabilistic or ensemble streamflow forecasts. It has also been suggested in past studies that ensemble forecasts might possess a greater economic value than deterministic forecasts. However, the vast majority of recent hydro-economic literature is based on the cost-loss ratio framework, which might be appealing for its simplicity and intuitiveness. One important drawback of the cost-loss ratio is that it implicitly assumes a risk-neutral decision maker. By definition, a risk-neutral individual is indifferent to forecasts' sharpness: as long as forecasts agree with observations on average, the risk-neutral individual is satisfied. A risk-averse individual, however, is sensitive to the level of precision (sharpness) of forecasts. This person is willing to pay to increase his or her certainty about future events. In fact, this is how insurance companies operate: the probability of seeing one's house burn down is relatively low, so the expected cost related to such event is also low. However, people are willing to buy insurance to avoid the risk, however small, of loosing everything. Similarly, in a context where people's safety and property is at stake, the typical decision maker is more risk-averse than risk-neutral. Consequently, the cost-loss ratio is not the most appropriate tool to assess the economic value of flood forecasts. This presentation describes a more realistic framework for assessing the economic value of such forecasts for flood mitigation purposes. Borrowing from economics, the Constant Absolute Risk Aversion utility function (CARA) is the central tool of this new framework. Utility functions allow explicitly accounting for the level of risk aversion of the decision maker and fully exploiting the information related to ensemble forecasts' uncertainty. Three concurrent ensemble streamflow forecasting systems are compared in terms of quality (comparison with
Zhou, S. L.; McMahon, T. A.; Walton, A.; Lewis, J.
A time series forecasting model of hourly water consumption 24 h in advance for an urban zone within the Melbourne (Australia) water supply system is developed. The model comprises two modules—daily and hourly. The daily module is formulated as a set of equations representing the effects of three factors on water use namely seasonality, climatic correlation, and autocorrelation. The hourly module is developed to disaggregate the estimated daily consumption into hourly consumption. The models were calibrated using hourly and daily data for a 6 year period, and independently validated over an additional seven month period. Over this latter period, the hourly forecast model accounted for 66% of the variance in the peak hourly water consumption with a standard error of 162 l/p/d.
Beria, H.; Nanda, T., Sr.; Chatterjee, C.
High resolution satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM), Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts (ECMWF), etc., offer a promising alternative to flood forecasting in data scarce regions. At the current state-of-art, these products cannot be used in the raw form for flood forecasting, even at smaller lead times. In the current study, these precipitation products are bias corrected using statistical techniques, such as additive and multiplicative bias corrections, and wavelet multi-resolution analysis (MRA) with India Meteorological Department (IMD) gridded precipitation product,obtained from gauge-based rainfall estimates. Neural network based rainfall-runoff modeling using these bias corrected products provide encouraging results for flood forecasting upto 48 hours lead time. We will present various statistical and graphical interpretations of catchment response to high rainfall events using both the raw and bias corrected precipitation products at different lead times.
Wardah, T.; Abu Bakar, S. H.; Bardossy, A.; Maznorizan, M.
SummaryFrequent flash-floods causing immense devastation in the Klang River Basin of Malaysia necessitate an improvement in the real-time forecasting systems being used. The use of meteorological satellite images in estimating rainfall has become an attractive option for improving the performance of flood forecasting-and-warning systems. In this study, a rainfall estimation algorithm using the infrared (IR) information from the Geostationary Meteorological Satellite-5 (GMS-5) is developed for potential input in a flood forecasting system. Data from the records of GMS-5 IR images have been retrieved for selected convective cells to be trained with the radar rain rate in a back-propagation neural network. The selected data as inputs to the neural network, are five parameters having a significant correlation with the radar rain rate: namely, the cloud-top brightness-temperature of the pixel of interest, the mean and the standard deviation of the temperatures of the surrounding five by five pixels, the rate of temperature change, and the sobel operator that indicates the temperature gradient. In addition, three numerical weather prediction (NWP) products, namely the precipitable water content, relative humidity, and vertical wind, are also included as inputs. The algorithm is applied for the areal rainfall estimation in the upper Klang River Basin and compared with another technique that uses power-law regression between the cloud-top brightness-temperature and radar rain rate. Results from both techniques are validated against previously recorded Thiessen areal-averaged rainfall values with coefficient correlation values of 0.77 and 0.91 for the power-law regression and the artificial neural network (ANN) technique, respectively. An extra lead time of around 2 h is gained when the satellite-based ANN rainfall estimation is coupled with a rainfall-runoff model to forecast a flash-flood event in the upper Klang River Basin.
Ruch, C; Stadler, H
The present paper deals with the implementation of online data transferred via LEO satellite communication in a flood forecasting system. Although the project is ongoing, it is already recognised that the information chain: "measurement-transmission-forecast-alert" can be shortened, i.e., the flood danger can be more rapidly communicated to the population at risk. This gain is particularly valuable for medium size catchments where the concentration time (basin time of response to rainfall) is short.
Zambrano, Luis; Pacheco-Muñoz, Rodrigo; Fernández, Tania
Floods in cities are increasingly common as a consequence of multifactor watershed dynamics, including geomorphology, land-use changes and land subsidence. However, urban managers have focused on infrastructure to address floods by reducing blocked sewage infrastructure, without significant success. Using Mexico City as a case study, we generated a spatial flood risk model with geomorphology and anthropogenic variables. The results helped contrast the implications of different public policies in land use and waste disposal, and correlating them with flood hazards. Waste disposal was only related to small floods. 58% of the city has a high risk of experiencing small floods, and 24% of the city has a risk for large floods. Half of the population with the lowest income is located in the high-risk areas for large floods. These models are easy to build, generate fast results and are able to help to flood policies, by understanding flood interactions in urban areas within the watershed.
Full Text Available The Normal Quantile Transform (NQT has been used in many hydrological and meteorological applications in order to make the Cumulated Distribution Function (CDF of the observed, simulated and forecast river discharge, water level or precipitation data Gaussian. It is also the heart of the meta-Gaussian model for assessing the total predictive uncertainty of the Hydrological Uncertainty Processor (HUP developed by Krzysztofowicz. In the field of geo-statistics this transformation is better known as the Normal-Score Transform. In this paper some possible problems caused by small sample sizes when applying the NQT in flood forecasting systems will be discussed and a novel way to solve the problem will be outlined by combining extreme value analysis and non-parametric regression methods. The method will be illustrated by examples of hydrological stream-flow forecasts.
Chen, S. S.
The 2017 hurricane season is estimated as one of the costliest in the U.S. history. The damage and devastation caused by Hurricane Harvey in Houston, Irma in Florida, and Maria in Puerto Rico are distinctly different in nature. The complexity of hurricane impacts from extreme wind, rain, storm surge, and flooding presents a major challenge in hurricane forecasting. A detailed comparison of the storm impacts from Harvey, Irma, and Maria will be presented using observations and state-of-the-art new generation coupled atmosphere-wave-ocean hurricane forecast model. The author will also provide an overview on what we can expect in terms of advancement in science and technology that can help improve hurricane impact forecast in the near future.
Nuno Eduardo Simões
Full Text Available It is a common practice to assign the return period of a given storm event to the urban pluvial flood event that such storm generates. However, this approach may be inappropriate as rainfall events with the same return period can produce different urban pluvial flooding events, i.e., with different associated flood extent, water levels and return periods. This depends on the characteristics of the rainfall events, such as spatial variability, and on other characteristics of the sewer system and the catchment. To address this, the paper presents an innovative contribution to produce stochastic urban pluvial flood hazard maps. A stochastic rainfall generator for urban-scale applications was employed to generate an ensemble of spatially—and temporally—variable design storms with similar return period. These were used as input to the urban drainage model of a pilot urban catchment (~9 km2 located in London, UK. Stochastic flood hazard maps were generated through a frequency analysis of the flooding generated by the various storm events. The stochastic flood hazard maps obtained show that rainfall spatial-temporal variability is an important factor in the estimation of flood likelihood in urban areas. Moreover, as compared to the flood hazard maps obtained by using a single spatially-uniform storm event, the stochastic maps generated in this study provide a more comprehensive assessment of flood hazard which enables better informed flood risk management decisions.
Deng, Liping; McCabe, Matthew; Stenchikov, Georgiy L.; Evans, Jason P.; Kucera, Paul A.
The challenges of monitoring and forecasting flash-flood-producing storm events in data-sparse and arid regions are explored using the Weather Research and Forecasting (WRF) Model (version 3.5) in conjunction with a range of available satellite
Ercolani, G.; Castelli, F.
Flood early warning systems benefit from accurate river flow forecasts, and data assimilation may improve their reliability. However, the actual enhancement that can be obtained in the operational practice should be investigated in detail and quantified. In this work we assess the benefits that the simultaneous assimilation of discharge observations at multiple locations can bring to flow forecasting through a distributed hydrologic model. The distributed model, MOBIDIC, is part of the operational flood forecasting chain of Tuscany Region in Central Italy. The assimilation system adopts a mixed variational-Monte Carlo approach to update efficiently initial river flow, soil moisture, and a parameter related to runoff production. The evaluation of the system is based on numerous hindcast experiments of real events. The events are characterized by significant rainfall that resulted in both high and relatively low flow in the river network. The area of study is the main basin of Tuscany Region, i.e. Arno river basin, which extends over about 8300 km2 and whose mean annual precipitation is around 800 mm. Arno's mainstream, with its nearly 240 km length, passes through major Tuscan cities, as Florence and Pisa, that are vulnerable to floods (e.g. flood of November 1966). The assimilation tests follow the usage of the model in the forecasting chain, employing the operational resolution in both space and time (500 m and 15 minutes respectively) and releasing new flow forecasts every 6 hours. The assimilation strategy is evaluated in respect to open loop simulations, i.e. runs that do not exploit discharge observations through data assimilation. We compare hydrographs in their entirety, as well as classical performance indexes, as error on peak flow and Nash-Sutcliffe efficiency. The dependence of performances on lead time and location is assessed. Results indicate that the operational forecasting chain can benefit from the developed assimilation system, although with a
Löwe, Roland; Sto Domingo, Nina; Urich, Christian
to use the results of the hydraulic simulation to condition DANCE4WATER and to account for flood risk in the simulated urban development. In an Australian case study, we demonstrate that future flood risk can be significantly reduced while maintaining the overall speed of urban development.......We have developed a setup that couples the urban development model DANCE4WATER with the 1D-2D hydraulic model MIKE FLOOD. The setup makes it possible to assess the impact of urban development and infrastructural change scenarios on flood risk in an automated manner. In addition, it permits us...
Van Steenbergen, N.; Willems, P.
Reliable flood forecasts are the most important non-structural measures to reduce the impact of floods. However flood forecasting systems are subject to uncertainty originating from the input data, model structure and model parameters of the different hydraulic and hydrological submodels. To quantify this uncertainty a non-parametric data-based approach has been developed. This approach analyses the historical forecast residuals (differences between the predictions and the observations at river gauging stations) without using a predefined statistical error distribution. Because the residuals are correlated with the value of the forecasted water level and the lead time, the residuals are split up into discrete classes of simulated water levels and lead times. For each class, percentile values are calculated of the model residuals and stored in a 'three dimensional error' matrix. By 3D interpolation in this error matrix, the uncertainty in new forecasted water levels can be quantified. In addition to the quantification of the uncertainty, the communication of this uncertainty is equally important. The communication has to be done in a consistent way, reducing the chance of misinterpretation. Also, the communication needs to be adapted to the audience; the majority of the larger public is not interested in in-depth information on the uncertainty on the predicted water levels, but only is interested in information on the likelihood of exceedance of certain alarm levels. Water managers need more information, e.g. time dependent uncertainty information, because they rely on this information to undertake the appropriate flood mitigation action. There are various ways in presenting uncertainty information (numerical, linguistic, graphical, time (in)dependent, etc.) each with their advantages and disadvantages for a specific audience. A useful method to communicate uncertainty of flood forecasts is by probabilistic flood mapping. These maps give a representation of the
Piliksere, A.; Sennikovs, J.; Virbulis, J.; Bethers, U.; Bethers, P.; Valainis, A.
Riga, the capital of Latvia, is located on River Daugava at the Gulf of Riga. The main flooding risks of Riga city are: (1) storm caused water setup in South part of Gulf of Riga (storm event), (2) water level increase caused by Daugava River discharge maximums (spring snow melting event) and (3) strong rainfall or rapid snow melting in densely populated urban areas. The first two flooding factors were discussed previously (Piliksere et al, 2011). The aims of the study were (1) the identification of the flood risk situations in densely populated areas, (2) the quantification of the flooding scenarios caused by rain and snow melting events of different return periods nowadays, in the near future (2021-2050), far future (2071-2100) taking into account the projections of climate change, (3) estimation of groundwater level for Riga city, (4) the building and calibration of the hydrological mathematical model based on SWMM (EPA, 2004) for the domain potentially vulnerable for rain and snow melt flooding events, (5) the calculation of rain and snow melting flood events with different return periods, (6) mapping the potentially flooded areas on a fine grid. The time series of short term precipitation events during warm time period of year (id est. rain events) were analyzed for 35 year long time period. Annual maxima of precipitation intensity for events with different duration (5 min; 15 min; 1h; 3h; 6h; 12h; 1 day; 2 days; 4 days; 10 days) were calculated. The time series of long term simultaneous precipitation data and observations of the reduction of thickness of snow cover were analyzed for 27 year long time period. Snow thawing periods were detected and maximum of snow melting intensity for events with different intensity (1day; 2 days; 4 days; 7 days; 10 days) were calculated. According to the occurrence probability six scenarios for each event for nowadays, near and far future with return period once in 5, 10, 20, 50, 100 and 200 years were constructed based on
Jahr Hegdahl, Trine; Steinsland, Ingelin; Merete Tallaksen, Lena; Engeland, Kolbjørn
Probabilistic flood forecasting has an added value for decision making. The Norwegian flood forecasting service is based on a flood forecasting model that run for 145 basins. Covering all of Norway the basins differ in both size and hydrological regime. Currently the flood forecasting is based on deterministic meteorological forecasts, and an auto-regressive procedure is used to achieve probabilistic forecasts. An alternative approach is to use meteorological and hydrological ensemble forecasts to quantify the uncertainty in forecasted streamflow. The hydrological ensembles are based on forcing a hydrological model with meteorological ensemble forecasts of precipitation and temperature. However, the ensembles of precipitation are often biased and the spread is too small, especially for the shortest lead times, i.e. they are not calibrated. These properties will, to some extent, propagate to hydrological ensembles, that most likely will be uncalibrated as well. Pre- and post-processing methods are commonly used to obtain calibrated meteorological and hydrological ensembles respectively. Quantitative studies showing the effect of the combined processing of the meteorological (pre-processing) and the hydrological (post-processing) ensembles are however few. The aim of this study is to evaluate the influence of pre- and post-processing on the skill of streamflow predictions, and we will especially investigate if the forecasting skill depends on lead-time, basin size and hydrological regime. This aim is achieved by applying the 51 medium-range ensemble forecast of precipitation and temperature provided by the European Center of Medium-Range Weather Forecast (ECMWF). These ensembles are used as input to the operational Norwegian flood forecasting model, both raw and pre-processed. Precipitation ensembles are calibrated using a zero-adjusted gamma distribution. Temperature ensembles are calibrated using a Gaussian distribution and altitude corrected by a constant gradient
Full Text Available This study evaluated the application of the European flood forecasting operational real time system (EFFORTS to the Yellow River. An automatic data pre-processing program was developed to provide real-time hydrometeorological data. Various GIS layers were collected and developed to meet the demands of the distributed hydrological model in the EFFORTS. The model parameters were calibrated and validated based on more than ten years of historical hydrometeorological data from the study area. The San-Hua Basin (from the Sanmenxia Reservoir to the Huayuankou Hydrological Station, the most geographically important area of the Yellow River, was chosen as the study area. The analysis indicates that the EFFORTS enhances the work efficiency, extends the flood forecasting lead time, and attains an acceptable level of forecasting accuracy in the San-Hua Basin, with a mean deterministic coefficient at Huayuankou Station, the basin outlet, of 0.90 in calibration and 0.96 in validation. The analysis also shows that the simulation accuracy is better for the southern part than for the northern part of the San-Hua Basin. This implies that, along with the characteristics of the basin and the mechanisms of runoff generation of the hydrological model, the hydrometeorological data play an important role in simulation of hydrological behavior.
Cole, Steven J.; Moore, Robert J.; Wells, Steven C.
Across the world, there is increasing demand for more robust and timely forecast and alert information on Surface Water Flooding (SWF). Within a UK context, the government Pitt Review into the Summer 2007 floods provided recommendations and impetus to improve the understanding of SWF risk for both off-line design and real-time forecasting and warning. Ongoing development and trial of an end-to-end real-time SWF system is being progressed through the recently formed Natural Hazards Partnership (NHP) with delivery to the Flood Forecasting Centre (FFC) providing coverage over England & Wales. The NHP is a unique forum that aims to deliver coordinated assessments, research and advice on natural hazards for governments and resilience communities across the UK. Within the NHP, a real-time Hazard Impact Model (HIM) framework has been developed that includes SWF as one of three hazards chosen for initial trialling. The trial SWF HIM system uses dynamic gridded surface-runoff estimates from the Grid-to-Grid (G2G) hydrological model to estimate the SWF hazard. National datasets on population, infrastructure, property and transport are available to assess impact severity for a given rarity of SWF hazard. Whilst the SWF hazard footprint is calculated in real-time using 1, 3 and 6 hour accumulations of G2G surface runoff on a 1 km grid, it has been possible to associate these with the effective rainfall design profiles (at 250m resolution) used as input to a detailed flood inundation model (JFlow+) run offline to produce hazard information resolved to 2m resolution. This information is contained in the updated Flood Map for Surface Water (uFMfSW) held by the Environment Agency. The national impact datasets can then be used with the uFMfSW SWF hazard dataset to assess impacts at this scale and severity levels of potential impact assigned at 1km and for aggregated county areas in real-time. The impact component is being led by the Health and Safety Laboratory (HSL) within the NHP
Löwe, Roland; Urich, Christian; Sto. Domingo, Nina; Mark, Ole; Deletic, Ana; Arnbjerg-Nielsen, Karsten
We present a new framework for flexible testing of flood risk adaptation strategies in a variety of urban development and climate scenarios. This framework couples the 1D-2D hydrodynamic simulation package MIKE FLOOD with the agent-based urban development model DAnCE4Water and provides the possibility to systematically test various flood risk adaptation measures ranging from large infrastructure changes over decentralised water management to urban planning policies. We have tested the framework in a case study in Melbourne, Australia considering 9 scenarios for urban development and climate and 32 potential combinations of flood adaptation measures. We found that the performance of adaptation measures strongly depended on the considered climate and urban development scenario and the other implementation measures implemented, suggesting that adaptive strategies are preferable over one-off investments. Urban planning policies proved to be an efficient means for the reduction of flood risk, while implementing property buyback and pipe increases in a guideline-oriented manner was too costly. Random variations in location and time point of urban development could have significant impact on flood risk and would in some cases outweigh the benefits of less efficient adaptation strategies. The results of our setup can serve as an input for robust decision making frameworks and thus support the identification of flood risk adaptation measures that are economically efficient and robust to variations of climate and urban layout.
This thesis deals with the generation of probabilistic forecasts in urban hydrology. In particular, we focus on the case of runoff forecasting for real-time control (RTC) on horizons of up to two hours. For the generation of probabilistic on-line runoff forecasts, we apply the stochastic grey...... and forecasts have on on-line runoff forecast quality. Finally, we implement the stochastic grey-box model approach in a real-world real-time control (RTC) setup and study how RTC can benefit from a dynamic quantification of runoff forecast uncertainty....
Rodríguez-Gaviria, E. M.; Botero-Fernandez, V.
Flood risk management in small urban areas in Colombia has a great degree of uncertainty due to the low availability and quality of data, the non-existent personnel qualified in the collection and processing of data, and the insufficient information to evaluate the risk and vulnerability. It is because of this that two methods are developed: one for the generation of flood threat maps for different return periods combining historical, geomorphological, and hydrological hydraulic methods assisted by remote sensors and SIG through the use of data acquired through field campaigns, official hydrological networks, orthophotos, multitemporal topographic maps, and ASTER, STRM, and LiDAR images. And another method in which categorical variables are established, linking local physical, social, economical, environmental and political-institutional factors that are explored through different media such as reports, news, databases, transects, interviews, community workshops, and surveys conducted at homes. Such variables were included within an analysis of multiple correspondence to conduct a descriptive study of the exposure, susceptibility, and capacity conditions and to create a vulnerability index that was spatially plotted spatially on maps. The uncertainty is reduced in the measure in which local knowledge is used as a source of information acquisition, of validation of what already exists, and of calibration of the proposed methods. This research was applied to the urban centers of Caucasia (Antioquia) and Plato (Magdalena), which have been historically affected by slow flooding of the Magdalena and Cauca river, it being especially useful in the selection of best alternatives for risk management, planning for development, and land use management, with the possibility of replicating it to benefit other municipalities that experience the same reality.
One of the commonest environmental hazards threatening food security now in Nigeria is flood. The study therefore investigated the effects of flood on farmers in peri – urban areas of Ibadan. Using a snow ball research method, 60 farmers were selected from the six local governments in the peri – urban areas of Ibadan and ...
Moore, Robert J.; Wells, Steven C.; Cole, Steven J.
It has been common for flood forecasting systems to be commissioned at a catchment or regional level in response to local priorities and hydrological conditions, leading to variety in system design and model choice. As systems mature and efficiencies of national management are sought, there can be a drive towards system rationalisation, gaining an overview of model performance and consideration of simplification through model-type convergence. Flood forecasting model assessments, whilst overseen at a national level, may be commissioned and managed at a catchment and regional level, take a variety of forms and be large in number. This presents a challenge when an integrated national assessment is required to guide operational use of flood forecasts and plan future investment in flood forecasting models and supporting hydrometric monitoring. This contribution reports on how a nationally consistent framework for flood forecasting model performance has been developed to embrace many past, ongoing and future assessments for local river systems by engineering consultants across England & Wales. The outcome is a Performance Summary for every site model assessed which, on a single page, contains relevant catchment information for context, a selection of overlain forecast and observed hydrographs and a set of performance statistics with associated displays of novel condensed form. One display provides performance comparison with other models that may exist for the site. The performance statistics include skill scores for forecasting events (flow/level threshold crossings) of differing severity/rarity, indicating their probability and likely timing, which have real value in an operational setting. The local models assessed can be of any type and span rainfall-runoff (conceptual and transfer function) and flow routing (hydrological and hydrodynamic) forms. Also accommodated by the framework is the national G2G (Grid-to-Grid) distributed hydrological model, providing area
Barbetta, Silvia; Coccia, Gabriele; Moramarco, Tommaso; Todini, Ezio
The negative effects of severe flood events are usually contrasted through structural measures that, however, do not fully eliminate flood risk. Non-structural measures, such as real-time flood forecasting and warning, are also required. Accurate stage/discharge future predictions with appropriate forecast lead-time are sought by decision-makers for implementing strategies to mitigate the adverse effects of floods. Traditionally, flood forecasting has been approached by using rainfall-runoff and/or flood routing modelling. Indeed, both types of forecasts, cannot be considered perfectly representing future outcomes because of lacking of a complete knowledge of involved processes (Todini, 2004). Nonetheless, although aware that model forecasts are not perfectly representing future outcomes, decision makers are de facto implicitly assuming the forecast of water level/discharge/volume, etc. as "deterministic" and coinciding with what is going to occur. Recently the concept of Predictive Uncertainty (PU) was introduced in hydrology (Krzysztofowicz, 1999), and several uncertainty processors were developed (Todini, 2008). PU is defined as the probability of occurrence of the future realization of a predictand (water level/discharge/volume) conditional on: i) prior observations and knowledge, ii) the available information obtained on the future value, typically provided by one or more forecast models. Unfortunately, PU has been frequently interpreted as a measure of lack of accuracy rather than the appropriate tool allowing to take the most appropriate decisions, given a model or several models' forecasts. With the aim to shed light on the benefits for appropriately using PU, a multi-temporal approach based on the MCP approach (Todini, 2008; Coccia and Todini, 2011) is here applied to stage forecasts at sites along the Upper Tiber River. Specifically, the STAge Forecasting-Rating Curve Model Muskingum-based (STAFOM-RCM) (Barbetta et al., 2014) along with the Rating
Full Text Available In recent years, the Weather Research and Forecast (WRF model has been utilized to generate quantitative precipitation forecasts with higher spatial and temporal resolutions. However, factors including horizontal resolution, domain size, and the physical parameterization scheme have a strong impact on the dynamic downscaling ability of the WRF model. In this study, the influence of these factors has been analyzed in precipitation forecasting for the Xijiang Basin, southern China—a region with complex topography. The results indicate that higher horizontal resolutions always result in higher Critical Success Indexes (CSI, but higher biases as well. Meanwhile, the precipitation forecast skills are also influenced by the combination of microphysics parameterization scheme and cumulus convective parameterization scheme. On the basis of these results, an optimized configuration of the WRF model is built in which the horizontal resolution is 10 km, the microphysics parameterization is the Lin scheme, and the cumulus convective parameterization is the Betts–Miller–Janjic scheme. This configuration is then evaluated by simulating the daily weather during the 2013–2014 flood season. The high Critical Success Index scores and low biases at various thresholds and lead times confirm the high accuracy of the optimized WRF model configuration for Xijiang Basin. However, the performance of the WRF model varies from different sub-basins due to the complexity of the mesoscale convective system (MCS over this region.
Massimo Rossa, Andrea; Laudanna Del Guerra, Franco; Borga, Marco; Zanon, Francesco; Settin, Tommaso; Leuenberger, Daniel
Space and time scales of flash floods are such that flash flood forecasting and warning systems depend upon the accurate real-time provision of rainfall information, high-resolution numerical weather prediction (NWP) forecasts and the use of hydrological models. Currently available high-resolution NWP model models can potentially provide warning forecasters information on the future evolution of storms and their internal structure, thereby increasing convective-scale warning lead times. However, it is essential that the model be started with a very accurate representation of on-going convection, which calls for assimilation of high-resolution rainfall data. This study aims to assess the feasibility of using carefully checked radar-derived quantitative precipitation estimates (QPE) for assimilation into NWP and hydrological models. The hydrometeorological modeling chain includes the convection-permitting NWP model COSMO-2 and a hydrologic-hydraulic models built upon the concept of geomorphological transport. Radar rainfall observations are assimilated into the NWP model via the latent heat nudging method. The study is focused on 26 September 2007 extreme flash flood event which impacted the coastal area of north-eastern Italy around Venice. The hydro-meteorological modeling system is implemented over the Dese river, a 90 km2 catchment flowing to the Venice lagoon. The radar rainfall observations are carefully checked for artifacts, including beam attenuation, by means of physics-based correction procedures and comparison with a dense network of raingauges. The impact of the radar QPE in the assimilation cycle of the NWP model is very significant, in that the main individual organized convective systems were successfully introduced into the model state, both in terms of timing and localization. Also, incorrectly localized precipitation in the model reference run without rainfall assimilation was correctly reduced to about the observed levels. On the other hand, the
Kefi, M.; Binaya, M. K.; Kumar, P.; Fukushi, K.
Urbanization, changes in land use and global warming increase the threat of natural disasters such as flooding. In recent decades, it was observed a rise of intensity and frequency of flood events. The exposure both of people and the national economy to flood hazards is amplified and can induce serious economic and social damages. For this reason, local governments adopted several strategies to cope with flood risk in urban areas in particular, but a better comprehension of the flood hazard factors may enhance the efficiency of mitigating measures overall. For this research, a spatial analysis is applied to estimate future direct flood damage for 2030 in three Southeast Asian megacities: Jakarta (Indonesia), Metro-Manila (Philippines) and Hanoi (Vietnam). This comprehensive method combined flood characteristics (flood depth) obtained from flood simulation using FLO-2D, land use generated from supervised classification and remote sensing products, property value of affected buildings and flood damage rate derived from flood depth function. This function is established based on field surveys with local people affected by past flood events. Additionally, two scenarios were analyzed to simulate the future conditions. The first one is related to climate change and it is based on several General Circulation Models (GCMs). However, the second one is establish to point out the effect of adaptation strategies. The findings shows that the climate change combined with the expansion of built-up areas increase the vulnerability of urban areas to flooding and the economic damage. About 16%, 8% and 19% of flood inundation areas are expected to increase respectively in Metro-Manila, Jakarta and Hanoi. However, appropriate flood control measures can be helpful to reduce the impact of natural disaster. Furthermore, flood damage maps are generated at a large scale, which can be helpful to local stakeholders when prioritizing their mitigation strategies on urban disaster resilience.
Jørgensen, Claus; Mark, Ole; Djordjevic, Slobodan; Hammond, Michael; Khan, David M.; Erichsen, Anders; Dorrit Enevoldsen, Ann; Heinicke, Gerald; Helwigh, Birgitte
Indroduction Urban flooding due to rainfall exceeding the design capacity of drainage systems is a global problem and it has significant economic and social consequences. While the cost of the direct flood damages of urban flooding is well understood, the indirect damages, like the water borne diseases is in general still poorly understood. Climate changes are expected to increase the frequency of urban flooding in many countries which is likely to increase water borne diseases. Diarrheal diseases are most prevalent in developing countries, where poor sanitation, poor drinking water and poor surface water quality causes a high disease burden and mortality, especially during floods. The level of water borne diarrhea in countries with well-developed water and waste water infrastructure has been reduced to an acceptable level, and the population in general do not consider waste water as being a health risk. Hence, exposure to wastewater influenced urban flood water still has the potential to cause transmission of diarrheal diseases. When managing urban flooding and planning urban climate change adaptations, health risks are rarely taken into consideration. This paper outlines a novel methodology for linking dynamic urban flood modelling with Quantitative Microbial Risk Assessment (QMRA). This provides a unique possibility for understanding the interaction between urban flooding and the health risks caused by direct human contact with flood water and provides an option for reducing the burden of disease in the population through the use of intelligent urban flood risk management. Methodology We have linked hydrodynamic urban flood modelling with quantitative microbial risk assessment (QMRA) to determine the risk of infection caused by exposure to wastewater influenced urban flood water. The deterministic model MIKE Flood, which integrates the sewer network model in MIKE Urban and the 2D surface model MIKE21, was used to calculate the concentration of pathogens in the
Sadler, J. M.; Goodall, J. L.; Morsy, M. M.; Spencer, K.
Sea level rise has already caused more frequent and severe coastal flooding and this trend will likely continue. Flood prediction is an essential part of a coastal city's capacity to adapt to and mitigate this growing problem. Complex coastal urban hydrological systems however, do not always lend themselves easily to physically-based flood prediction approaches. This paper presents a method for using a data-driven approach to estimate flood severity in an urban coastal setting using crowd-sourced data, a non-traditional but growing data source, along with environmental observation data. Two data-driven models, Poisson regression and Random Forest regression, are trained to predict the number of flood reports per storm event as a proxy for flood severity, given extensive environmental data (i.e., rainfall, tide, groundwater table level, and wind conditions) as input. The method is demonstrated using data from Norfolk, Virginia USA from September 2010 to October 2016. Quality-controlled, crowd-sourced street flooding reports ranging from 1 to 159 per storm event for 45 storm events are used to train and evaluate the models. Random Forest performed better than Poisson regression at predicting the number of flood reports and had a lower false negative rate. From the Random Forest model, total cumulative rainfall was by far the most dominant input variable in predicting flood severity, followed by low tide and lower low tide. These methods serve as a first step toward using data-driven methods for spatially and temporally detailed coastal urban flood prediction.
Saad, H.; Habib, E. H.
In August 2016, the city of Lafayette and many other urban centers in south Louisiana experienced catastrophic flooding resulting from prolonged rainfall. Statewide, this historic storm displaced more than 30,000 people from their homes, resulted in damages up to $8.7 billion, put rescue workers at risk, interrupted institutions of education and business, and worst of all, resulted in the loss of life of at least 13 Louisiana residents. With growing population and increasing signs of climate change, the frequency of major floods and severe storms is expected to increase, as will the impacts of these events on our communities. Local communities need improved capabilities for forecasting flood events, monitoring of flood impacts on roads and key infrastructure, and effectively communicating real-time flood dangers at scales that are useful to the public. The current study presents the application of the WRF-Hydro modeling system to represent integrated hydrologic, hydraulic and hydrometeorological processes that drive flooding in urban basins at temporal and spatial scales that can be useful to local communities. The study site is the 25- mile2 Coulee mine catchment in Lafayette, south Louisiana. The catchment includes two tributaries with natural streams located within mostly agricultural lands. The catchment crosses the I-10 highway and through the metropolitan area of the City of Lafayette into a man-made channel, which eventually drains into the Vermilion River and the Gulf of Mexico. Due to its hydrogeomorphic setting, local and rapid diversification of land uses, low elevation, and interdependent infrastructure, the integrated modeling of this coulee is considered a challenge. A nested multi-scale model is being built using the WRF-HYDRO, with 500m and 10m resolutions for the NOAH land-surface model and diffusive wave terrain routing grids, respectively.
Zhang, Ying; Guindon, Bert; Raymond, Don; Hong, Gang
The combination of rapid global urban growth and climate change has resulted in increased occurrence of major urban flood events across the globe. The distribution of flooded area is one of the key information layers for applications of emergency planning and response management. While SAR systems and technologies have been widely used for flood area delineation, radar images suffer from range ambiguities arising from corner reflection effects and shadowing in dense urban settings. A new mapping framework is proposed for the extraction and quantification of flood extent based on aerial optical multi-spectral imagery and ancillary data. This involves first mapping of flood areas directly visible to the sensor. Subsequently, the complete area of submergence is estimated from this initial mapping and inference techniques based on baseline data such as land cover and GIS information such as available digital elevation models. The methodology has been tested and proven effective using aerial photography for the case of the 2013 flood in Calgary, Canada.
Full Text Available The dynamic system response curve (DSRC is commonly applied as a real-time flood forecasting error correction method to improve the accuracy of real-time flood forecasting. It has been widely recognized that the least squares (OLS/LS method, employed by DSRC, breaks down ill-posed problems, and therefore, the DSRC method may lead to deterioration in performance caused by meaningless solutions. To address this problem, a diagnostically theoretical analysis was conducted to investigate the relationship between the numerical solution of the Fredholm equation of the first kind and the DSRC method. The analysis clearly demonstrates the derivation of the problem and has implications for an improved approach. To overcome the unstable problem, a new method using regularization techniques (Tikhonov regularization and L-Curve criterion is proposed. Moreover, in this study, to improve the performance of hydrological models, the new method is used as an error correction method to correct a variable from a hydrological model. The proposed method incorporates the information from a hydrological model structure. Based on the analysis of the hydrological model, the free water storage of the Xinanjiang rainfall-runoff (XAJ model is corrected to improve the model’s performance. A numerical example and a real case study are presented to compare the two methods. Results from the numerical example indicate that the mean Nash–Sutcliffe efficiency value (NSE of the regularized DSRC method (RDSRC decreased from 0.99 to 0.55, while the mean NSE of DSRC decreased from 0.98 to −1.84 when the noise level was increased. The overall performance measured by four different criteria clearly demonstrates the robustness of the RDSRC method. Similar results were obtained for the real case study. The mean NSE of 35 flood events obtained by RDSRC method was 0.92, which is significantly higher than the mean NSE of DSRC (0.7. The results demonstrate that the RDSRC method is much
Full Text Available Flood risk assessment is an essential part of flood risk management. As part of the new EU flood directive it is becoming increasingly more popular in European flood policy. Particularly cities with a high concentration of people and goods are vulnerable to floods. This paper introduces the adaptation of a novel method of multicriteria flood risk assessment, that was recently developed for the more rural Mulde river basin, to a city. The study site is Leipzig, Germany. The "urban" approach includes a specific urban-type set of economic, social and ecological flood risk criteria, which focus on urban issues: population and vulnerable groups, differentiated residential land use classes, areas with social and health care but also ecological indicators such as recreational urban green spaces. These criteria are integrated using a "multicriteria decision rule" based on an additive weighting procedure which is implemented into the software tool FloodCalc urban. Based on different weighting sets we provide evidence of where the most flood-prone areas are located in a city. Furthermore, we can show that with an increasing inundation extent it is both the social and the economic risks that strongly increase.
Zhou, Jianzhong; Zhang, Hairong; Zhang, Jianyun; Zeng, Xiaofan; Ye, Lei; Liu, Yi; Tayyab, Muhammad; Chen, Yufan
An accurate flood forecasting with long lead time can be of great value for flood prevention and utilization. This paper develops a one-way coupled hydro-meteorological modeling system consisting of the mesoscale numerical weather model Weather Research and Forecasting (WRF) model and the Chinese Xinanjiang hydrological model to extend flood forecasting lead time in the Jinshajiang River Basin, which is the largest hydropower base in China. Focusing on four typical precipitation events includes: first, the combinations and mode structures of parameterization schemes of WRF suitable for simulating precipitation in the Jinshajiang River Basin were investigated. Then, the Xinanjiang model was established after calibration and validation to make up the hydro-meteorological system. It was found that the selection of the cloud microphysics scheme and boundary layer scheme has a great impact on precipitation simulation, and only a proper combination of the two schemes could yield accurate simulation effects in the Jinshajiang River Basin and the hydro-meteorological system can provide instructive flood forecasts with long lead time. On the whole, the one-way coupled hydro-meteorological model could be used for precipitation simulation and flood prediction in the Jinshajiang River Basin because of its relatively high precision and long lead time.
Galelli, Stefano; Goedbloed, Albert; Schmitter, Petra; Castelletti, Andrea
Urban water reservoirs are a viable adaptation option to account for increasing drinking water demand of urbanized areas as they allow storage and re-use of water that is normally lost. In addition, the direct availability of freshwater reduces pumping costs and diversifies the portfolios of drinking water supply. Yet, these benefits have an associated twofold cost. Firstly, the presence of large, impervious areas increases the hydraulic efficiency of urban catchments, with short time of concentration, increased runoff rates, losses of infiltration and baseflow, and higher risk of flash floods. Secondly, the high concentration of nutrients and sediments characterizing urban discharges is likely to cause water quality problems. In this study we propose a new control scheme combining Model Predictive Control (MPC), hydro-meteorological forecasts and dynamic model emulation to design real-time operating policies that conjunctively optimize water quantity and quality targets. The main advantage of this scheme stands in its capability of exploiting real-time hydro-meteorological forecasts, which are crucial in such fast-varying systems. In addition, the reduced computational requests of the MPC scheme allows coupling it with dynamic emulators of water quality processes. The approach is demonstrated on Marina Reservoir, a multi-purpose reservoir located in the heart of Singapore and characterized by a large, highly urbanized catchment with a short (i.e. approximately one hour) time of concentration. Results show that the MPC scheme, coupled with a water quality emulator, provides a good compromise between different operating objectives, namely flood risk reduction, drinking water supply and salinity control. Finally, the scheme is used to assess the effect of source control measures (e.g. green roofs) aimed at restoring the natural hydrological regime of Marina Reservoir catchment.
Reed, P. M.; Chaney, N.; Herman, J. D.; Wood, E. F.; Ferringer, M. P.
This research represents a multi-institutional collaboration between Cornell University, The Aerospace Corporation, and Princeton University that has completed a Petascale diagnostic assessment of the current 10 satellite missions providing rainfall observations. Our diagnostic assessment has required four core tasks: (1) formally linking high-resolution astrodynamics design and coordination of space assets with their global hydrological impacts within a Petascale "many-objective" global optimization framework, (2) developing a baseline diagnostic evaluation of a 1-degree resolution global implementation of the Variable Infiltration Capacity (VIC) model to establish the required satellite observation frequencies and coverage to maintain acceptable global flood forecasts, (3) evaluating the limitations and vulnerabilities of the full suite of current satellite precipitation missions including the recently approved Global Precipitation Measurement (GPM) mission, and (4) conceptualizing the next generation spaced-based platforms for water cycle observation. Our team exploited over 100 Million hours of computing access on the 700,000+ core Blue Waters machine to radically advance our ability to discover and visualize key system tradeoffs and sensitivities. This project represents to our knowledge the first attempt to develop a 10,000 member Monte Carlo global hydrologic simulation at one degree resolution that characterizes the uncertain effects of changing the available frequencies of satellite precipitation on drought and flood forecasts. The simulation—optimization components of the work have set a theoretical baseline for the best possible frequencies and coverages for global precipitation given unlimited investment, broad international coordination in reconfiguring existing assets, and new satellite constellation design objectives informed directly by key global hydrologic forecasting requirements. Our research poses a step towards realizing the integrated
Valverde, Angel Luis Aldana; Beato, Ana Martinez Perez
In this paper a case study related to flood propagation forecast in the Segura River in Spain is presented along with the application that was developed for that purpose. Simulation and forecast models ease the work carry out by the watershed organism personnel and may be essential to understand the complexity of some of the propagation phenomena that take place at specific locations such as the study area, a man-made channel at the downstream end of the Segura River (from Contraparada to Guardamar), including the tributaries along the stream. Three different models were used in the previous studies: a steady state numerical model (Hec-Ras), a physical model and two unsteady state numerical models (ISIS and HMS). Also, historical time series were analyzed and some topography works were carried out along the stream. PROC Segura model was conceived for real time flood propagation forecast in the mentioned area using the data collected by the SAIH. A simplified model was developed based on the following methods: Muskingum, Muskingum-Cunge and Modified Puls. To overcome some of these models limitations, such as the one to one discharge-water surface relationships and the impossibility of reproducing downstream backwater, doubled input rating curves were used to estimate the discharge at some of the gauging stations located at the tributaries, i.e. Merancho and Rambia del Derramador, which may be affected by the water level in the Segura River. The advantages of using these simplified models versus a dynamic wave model were studied and reported as well. In general, it can be stated that when several solutions are provided to solve the same problem, the simplest solution is usually the best one.(Author)
M, A. B.; Lohani, B.; Jain, A.
Global climatic change has triggered weather patterns which lead to heavy and sudden rainfall in different parts of world. The impact of heavy rainfall is severe especially on urban areas in the form of urban flooding. In order to understand the effect of heavy rainfall induced flooding, it is necessary to model the entire flooding scenario more accurately, which is now becoming possible with the availability of high resolution airborne LiDAR data and other real time observations. However, there is not much understanding on the optimal use of these data and on the effect of other parameters on the performance of the flood model. This study aims at developing understanding on these issues. In view of the above discussion, the aim of this study is to (i) understand that how the use of high resolution LiDAR data improves the performance of urban flood model, and (ii) understand the sensitivity of various hydrological parameters on urban flood modelling. In this study, modelling of flooding in urban areas due to heavy rainfall is carried out considering Indian Institute of Technology (IIT) Kanpur, India as the study site. The existing model MIKE FLOOD, which is accepted by Federal Emergency Management Agency (FEMA), is used along with the high resolution airborne LiDAR data. Once the model is setup it is made to run by changing the parameters such as resolution of Digital Surface Model (DSM), manning's roughness, initial losses, catchment description, concentration time, runoff reduction factor. In order to realize this, the results obtained from the model are compared with the field observations. The parametric study carried out in this work demonstrates that the selection of catchment description plays a very important role in urban flood modelling. Results also show the significant impact of resolution of DSM, initial losses and concentration time on urban flood model. This study will help in understanding the effect of various parameters that should be part of a
Batelis, Stamatios-Christos; Rosolem, Rafael; Han, Dawei; Rahman, Mostaquimur
Land Surface Models (LSM) are based on physics principles and simulate the exchanges of energy, water and biogeochemical cycles between the land surface and lower atmosphere. Such models are typically applied for climate studies or effects of land use changes but as the resolution of LSMs and supporting observations are continuously increasing, its representation of hydrological processes need to be addressed adequately. For example, changes in climate and land use can alter the hydrology of a region, for instance, by altering its flooding regime. LSMs can be a powerful tool because of their ability to spatially represent a region with much finer resolution. However, despite such advantages, its performance has not been extensively assessed for flood forecasting simply because its representation of typical hydrological processes, such as overland flow and river routing, are still either ignored or roughly represented. In this study, we initially test the Joint UK Land Environment Simulator (JULES) as a flood forecast tool focusing on its river routing scheme. In particular, JULES river routing parameterization is based on the Rapid Flow Model (RFM) which relies on six prescribed parameters (two surface and two subsurface wave celerities, and two return flow fractions). Although this routing scheme is simple, the prescription of its six default parameters is still too generalized. Our aim is to understand the importance of each RFM parameter in a series of JULES simulations at a number of catchments in the UK for the 2006-2015 period. This is carried out, for instance, by making a number of assumptions of parameter behaviour (e.g., spatially uniform versus varying and/or temporally constant or time-varying parameters within each catchment). Hourly rainfall radar in combination with the CHESS (Climate, Hydrological and Ecological research Support System) meteorological daily data both at 1 km2 resolution are used. The evaluation of the model is based on hourly runoff
Full Text Available In the context of global urbanization, urban flood risk in many cities has become a serious environmental issue, threatening the health of residents and the environment. A number of hydrological studies have linked urban flooding issues closely to the spectrum of spatial patterns of urbanization, but relatively little attention has been given to small-scale catchments within the realm of urban systems. This study aims to explore the hydrological effects of small-scaled urbanized catchments assigned with various landscape patterns. Twelve typical residential catchments in Beijing were selected as the study areas. Total Impervious Area (TIA, Directly Connected Impervious Area (DCIA, and a drainage index were used as the catchment spatial metrics. Three scenarios were designed as different spatial arrangement of catchment imperviousness. Runoff variables including total and peak runoff depth (Qt and Qp were simulated by using Strom Water Management Model (SWMM. The relationship between catchment spatial patterns and runoff variables were determined, and the results demonstrated that, spatial patterns have inherent influences on flood risks in small urbanized catchments. Specifically: (1 imperviousness acts as an effective indicator in affecting both Qt and Qp; (2 reducing the number of rainwater inlets appropriately will benefit the catchment peak flow mitigation; (3 different spatial concentrations of impervious surfaces have inherent influences on Qp. These findings provide insights into the role of urban spatial patterns in driving rainfall-runoff processes in small urbanized catchments, which is essential for urban planning and flood management.
Yao, Lei; Chen, Liding; Wei, Wei
In the context of global urbanization, urban flood risk in many cities has become a serious environmental issue, threatening the health of residents and the environment. A number of hydrological studies have linked urban flooding issues closely to the spectrum of spatial patterns of urbanization, but relatively little attention has been given to small-scale catchments within the realm of urban systems. This study aims to explore the hydrological effects of small-scaled urbanized catchments assigned with various landscape patterns. Twelve typical residential catchments in Beijing were selected as the study areas. Total Impervious Area (TIA), Directly Connected Impervious Area (DCIA), and a drainage index were used as the catchment spatial metrics. Three scenarios were designed as different spatial arrangement of catchment imperviousness. Runoff variables including total and peak runoff depth (Qt and Qp) were simulated by using Strom Water Management Model (SWMM). The relationship between catchment spatial patterns and runoff variables were determined, and the results demonstrated that, spatial patterns have inherent influences on flood risks in small urbanized catchments. Specifically: (1) imperviousness acts as an effective indicator in affecting both Qt and Qp; (2) reducing the number of rainwater inlets appropriately will benefit the catchment peak flow mitigation; (3) different spatial concentrations of impervious surfaces have inherent influences on Qp. These findings provide insights into the role of urban spatial patterns in driving rainfall-runoff processes in small urbanized catchments, which is essential for urban planning and flood management. PMID:28264521
Akyurek, Z.; Bozoglu, B.; Girayhan, T.
Flooding has the potential to cause significant impacts to economic activities as well as to disrupt or displace populations. Changing climate regimes such as extreme precipitation events increase flood vulnerability and put additional stresses on infrastructure. In this study the flood modelling in an urbanized area, namely Samsun-Terme in Blacksea region of Turkey is done. MIKE21 with flexible grid is used in 2- dimensional shallow water flow modelling. 1/1000 scaled maps with the buildings for the urbanized area and 1/5000 scaled maps for the rural parts are used to obtain DTM needed in the flood modelling. The bathymetry of the river is obtained from additional surveys. The main river passing through the urbanized area has a capacity of Q5 according to the design discharge obtained by simple ungauged discharge estimation depending on catchment area only. The effects of the available structures like bridges across the river on the flooding are presented. The upstream structural measures are studied on scenario basis. Four sub-catchments of Terme River are considered as contributing the downstream flooding. The existing circumstance of the Terme River states that the meanders of the river have a major effect on the flood situation and lead to approximately 35% reduction in the peak discharge between upstream and downstream of the river. It is observed that if the flow from the upstream catchments can be retarded through a detention pond constructed in at least two of the upstream catchments, estimated Q100 flood can be conveyed by the river without overtopping from the river channel. The operation of the upstream detention ponds and the scenarios to convey Q500 without causing flooding are also presented. Structural management measures to address changes in flood characteristics in water management planning are discussed. Flood risk is obtained by using the flood hazard maps and water depth-damage functions plotted for a variety of building types and occupancies
Liu, Li; Gao, Chao; Xuan, Weidong; Xu, Yue-Ping
Ensemble flood forecasts by hydrological models using numerical weather prediction products as forcing data are becoming more commonly used in operational flood forecasting applications. In this study, a hydrological ensemble flood forecasting system comprised of an automatically calibrated Variable Infiltration Capacity model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated. The hydrological model is optimized by the parallel programmed ε-NSGA II multi-objective algorithm. According to the solutions by ε-NSGA II, two differently parameterized models are determined to simulate daily flows and peak flows at each of the three hydrological stations. Then a simple yet effective modular approach is proposed to combine these daily and peak flows at the same station into one composite series. Five ensemble methods and various evaluation metrics are adopted. The results show that ε-NSGA II can provide an objective determination on parameter estimation, and the parallel program permits a more efficient simulation. It is also demonstrated that the forecasts from ECMWF have more favorable skill scores than other Ensemble Prediction Systems. The multimodel ensembles have advantages over all the single model ensembles and the multimodel methods weighted on members and skill scores outperform other methods. Furthermore, the overall performance at three stations can be satisfactory up to ten days, however the hydrological errors can degrade the skill score by approximately 2 days, and the influence persists until a lead time of 10 days with a weakening trend. With respect to peak flows selected by the Peaks Over Threshold approach, the ensemble means from single models or multimodels are generally underestimated, indicating that the ensemble mean can bring overall improvement in forecasting of flows. For
Seto, Karen C.; Güneralp, Burak; Hutyra, Lucy R.
Urban land-cover change threatens biodiversity and affects ecosystem productivity through loss of habitat, biomass, and carbon storage. However, despite projections that world urban populations will increase to nearly 5 billion by 2030, little is known about future locations, magnitudes, and rates of urban expansion. Here we develop spatially explicit probabilistic forecasts of global urban land-cover change and explore the direct impacts on biodiversity hotspots and tropical carbon biomass. ...
Papathanasiou, C.; Makropoulos, C.; Mimikou, M.
Floods and forest fires are two of the most devastating natural hazards with severe socioeconomic, environmental as well as aesthetic impacts on the affected areas. Traditionally, these hazards are examined from different perspectives and are thus investigated through different, independent systems, overlooking the fact that they are tightly interrelated phenomena. In fact, the same flood event is more severe, i.e. associated with increased runoff discharge and peak flow and decreased time to peak, if it occurs over a burnt area than that occurring over a land not affected by fire. Mediterranean periurban areas, where forests covered with flammable vegetation coexist with agricultural land and urban zones, are typical areas particularly prone to the combined impact of floods and forest fires. Hence, the accurate assessment and effective management of post-fire flood risk becomes an issue of priority. The research presented in this paper aims to develop a robust methodological framework, using state of art tools and modern technologies to support the estimation of the change in time of five representative hydrological parameters for post-fire conditions. The proposed methodology considers both longer- and short-term initial conditions in order to assess the dynamic evolution of the selected parameters. The research focuses on typical Mediterranean periurban areas that are subjected to both hazards and concludes with a set of equations that associate post-fire and pre-fire conditions for five Fire Severity (FS) classes and three soil moisture states. The methodology has been tested for several flood events on the Rafina catchment, a periurban catchment in Eastern Attica (Greece). In order to validate the methodology, simulated hydrographs were produced and compared against available observed data. Results indicate a close convergence of observed and simulated flows. The proposed methodology is particularly flexible and thus easily adaptable to catchments with similar
García, Darío; Baquerizo, Asunción; Ortega, Miguel; Herrero, Javier; Ángel Losada, Miguel
Torrential and heavy rains are frequent in Andalusia (Southern Spain) due to the characteristic Mediterranean climate (semi-arid areas). This, in combination with a massive occupation of floodable (river sides) and coastal areas, produces severe problems of management and damage to the population and social and economical activities when extreme events occur. Some of the most important problems are being produced during last years in Almería (Southeastern Andalusia). Between 27 and 28 September 2012 rainstorms characterized by 240mm in 24h (exceeding precipitation for a return period of 500 years) occurred. Antas River and Jático creek, that are normally dry, became raging torrents. The massive flooding of occupied areas resulted in eleven deaths and two missing in Andalucía, with a total estimated cost of all claims for compensation on the order of 197 million euros. This study presents a probabilistic flood forecasting tool including the effect of river and marine forcings. It is based on a distributed, physically-based hydrological model (WiMMed). For Almería the model has been calibrated with the largest event recorded in Cantoria gauging station (data since 1965) on 19 October 1973. It was then validated with the second strongest event (26 October 1977). Among the different results of the model, it can provide probability floods scenarios in Andalusia with up 10 days weather forecasts. The tool has been applied to Vera, a 15.000 inhabitants town located in the east of Almería along the Antas River at an altitude of 95 meters. Its main economic resource is the "beach and sun" based-tourism, which has experienced an enormous growth during last decades. Its coastal stretch has been completely built in these years, occupying floodable areas and constricting the channel and rivers mouths. Simulations of the model in this area for the 1973 event and published in March 2011 on the internet event already announced that the floods of September 2012 may occur.
Sörensen, Johanna; Mobini, Shifteh
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 Malmö. 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 Malmö. 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 Malmö 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 Malmö, and other cities with similar characteristics, are discussed.
Löwe, Roland; Urich, Christian; Sto Domingo, Nina
Flood risk in cities is strongly affected by the development of the city itself. Many studies focus on changes in the flood hazard as a result of, for example, changed degrees of sealing in the catchment or climatic changes. However, urban developments in flood prone areas can affect the exposure...... to the hazard and thus have large impacts on flood risk. Different urban socio-economic development scenarios, rainfall inputs and options for the mitigation of flood risk, quickly lead to a large number of scenarios that need to be considered in the planning of the development of a city. This calls...... for automated analyses that allow the planner to quickly identify if, when and how infrastructure should be modified. Such analysis, which accounts for the two-way interactions between city development and flood risk, is possible only to a limited extent in existing tools. We have developed a software framework...
Schaer, Caroline; Thiam, Mame Demba; Nygaard, Ivan
In Senegal, considerable development assistance has been allocated to addressing the problem of repeated flooding in urban areas, involving changing thematic objectives, from short-term disaster relief to wide-ranging sanitation and drainage programmes. In spite of these numerous flood management....... These include, but are not restricted to, the political and personal appropriation of flood management-related processes, the reinforcement of the dichotomy between central government and municipalities, and a fragmented institutional framework with overlapping institutions....
Solvang Johansen, Stian; Steinsland, Ingelin; Engeland, Kolbjørn
Running hydrological models with precipitation and temperature ensemble forcing to generate ensembles of streamflow is a commonly used method in operational hydrology. Evaluations of streamflow ensembles have however revealed that the ensembles are biased with respect to both mean and spread. Thus postprocessing of the ensembles is needed in order to improve the forecast skill. The aims of this study is (i) to to evaluate how postprocessing of streamflow ensembles works for Norwegian catchments within different hydrological regimes and to (ii) demonstrate how post processed streamflow ensembles are used operationally by a hydropower producer. These aims were achieved by postprocessing forecasted daily discharge for 10 lead-times for 20 catchments in Norway by using EPS forcing from ECMWF applied the semi-distributed HBV-model dividing each catchment into 10 elevation zones. Statkraft Energi uses forecasts from these catchments for scheduling hydropower production. The catchments represent different hydrological regimes. Some catchments have stable winter condition with winter low flow and a major flood event during spring or early summer caused by snow melting. Others has a more mixed snow-rain regime, often with a secondary flood season during autumn, and in the coastal areas, the stream flow is dominated by rain, and the main flood season is autumn and winter. For post processing, a Bayesian model averaging model (BMA) close to (Kleiber et al 2011) is used. The model creates a predictive PDF that is a weighted average of PDFs centered on the individual bias corrected forecasts. The weights are here equal since all ensemble members come from the same model, and thus have the same probability. For modeling streamflow, the gamma distribution is chosen as a predictive PDF. The bias correction parameters and the PDF parameters are estimated using a 30-day sliding window training period. Preliminary results show that the improvement varies between catchments depending
Åström, Helena Lisa Alexandra; Friis Hansen, P.; Garrè, Luca
Urban flooding introduces significant risk to society. Non-stationarity leads to increased uncertainty and this is challenging to include in actual decision-making. The primary objective of this study was to develop a risk assessment and decision support framework for pluvial urban flood risk under...... non-stationary conditions using an influence diagram (ID) which is a Bayesian network (BN) extended with decision and utility nodes. Non-stationarity is considered to be the influence of climate change where extreme precipitation patterns change over time. The overall risk is quantified in monetary...... terms expressed as expected annual damage. The network is dynamic in as much as it assesses risk at different points in time. The framework provides means for decision-makers to assess how different decisions on flood adaptation affect the risk now and in the future. The result from the ID was extended...
This paper describes the impact of altered land use on urban flooding in Northwest Indiana over a 10-year time span between 1992 and 2001. The studied watershed, Great Calumet Basin, is located on the south shore of Lake Michigan, which is well known as a highly industrialized area. The flood peaks ...
Rogers, B. C.; Bertram, N.; Gunn, Alex
This paper provides guidance on how to identify and design the most suitable climate adaptation strategies for enhancing the liveability and flood resilience of urban catchments. It presents findings from a case study of Elwood, a coastal Melbourne suburb regularly affected by flooding. The resea...
Reyes-Acevedo, Martin Alejandro; Flacke, J.; Brussel, M.J.G.
The Ayamama River basin in Istanbul is a densely populated urban area that is frequently impacted by flash floods causing damage to people and infrastructure. The IPCC expects that under climate change conditions, more intense precipitation will occur, leading to a higher risk of flash floods.
Sarmah, Tanaya; Das, Sutapa
Guwahati, the capital city of Assam and the gateway to the seven north-eastern Indian states, is located in the Brahmaputra valley-one of the most flood prone regions of the world. The city receives an average annual rainfall of 1688 mm and is highly vulnerable towards frequent urban floods because of uncontrolled dumping of solid waste and siltation have choked the natural water channels. This coupled with the absence of an integrated drainage network and rapid urbanisation causes floods in many parts of the city, after a quick downpour. Bharalu river is the main natural water channel of the city and Bharalu basin is the most vulnerable one. The present paper is an attempt to plan for urban flood mitigation, by designing an integrated drainage network for the Bharalu basin which includes the low-lying urbanized areas bordered by the Guwahati-Shillong Road, the Radha Gobindo Baruah Road and the Rajgarh Road. Data regarding land use, flood level, rainfall, urban pattern and vulnerability towards urban flood were collected from available literature, field survey to find highest water level for 11.4 km road stretch, expert opinion survey from 18 experts and feedback from 77 community elders who have been residing in the city since the 1980s. The Bharalu basin is divided into seven drainage blocks and storm run-off has been calculated based on the inputs. Seven different trapezoidal drainage sections were designed to form an integrated drainage network which is 'self-healing' to a certain extent. This can serve as a template for the other catchment basins and to design a drainage network for the entire Guwahati city, thereby reducing urban flood hazard to a significant extent. The study illustrates the necessity of an urban flood mitigation planning approach in sub-Himalayan urban settlements such as Guwahati. Copyright © 2017 Elsevier Ltd. All rights reserved.
Full Text Available Analysis of urban saturated power loads is helpful to coordinate urban power grid construction and economic social development. There are two different kinds of forecasting models: the logistic curve model focuses on the growth law of the data itself, while the multi-dimensional forecasting model considers several influencing factors as the input variables. To improve forecasting performance, a novel combined forecasting model for saturated power load analysis was proposed in this paper, which combined the above two models. Meanwhile, the weights of these two models in the combined forecasting model were optimized by employing a fruit fly optimization algorithm. Using Hubei Province as the example, the effectiveness of the proposed combined forecasting model was verified, demonstrating a higher forecasting accuracy. The analysis result shows that the power load of Hubei Province will reach saturation in 2039, and the annual maximum power load will reach about 78,630 MW. The results obtained from this proposed hybrid urban saturated power load analysis model can serve as a reference for sustainable development for urban power grids, regional economies, and society at large.
E. O. Agafonnikova
Full Text Available The dynamics features of the surface waters for the territory of the Crimea area of Krasnodar region in flash flood conditions have been studied. The parameters of flooding depending on the precipitation intensity have been defined.
Bartos, M. D.; Kerkez, B.; Noh, S.; Seo, D. J.
In this study, we develop and evaluate a high resolution urban flash flood monitoring system using a wireless sensor network (WSN), a real-time rainfall-runoff model, and spatially-explicit radar rainfall predictions. Flooding is the leading cause of natural disaster fatalities in the US, with flash flooding in particular responsible for a majority of flooding deaths. While many riverine flood models have been operationalized into early warning systems, there is currently no model that is capable of reliably predicting flash floods in urban areas. Urban flash floods are particularly difficult to model due to a lack of rainfall and runoff data at appropriate scales. To address this problem, we develop a wide-area flood-monitoring wireless sensor network for the Dallas-Fort Worth metroplex, and use this network to characterize rainfall-runoff response over multiple heterogeneous catchments. First, we deploy a network of 22 wireless sensor nodes to collect real-time stream stage measurements over catchments ranging from 2-80 km2 in size. Next, we characterize the rainfall-runoff response of each catchment by combining stream stage data with gage and radar-based precipitation measurements. Finally, we demonstrate the potential for real-time flash flood prediction by joining the derived rainfall-runoff models with real-time radar rainfall predictions. We find that runoff response is highly heterogeneous among catchments, with large variabilities in runoff response detected even among nearby gages. However, when spatially-explicit rainfall fields are included, spatial variability in runoff response is largely captured. This result highlights the importance of increased spatial coverage for flash flood prediction.
Mahmoud, Shereif H; Gan, Thian Yew
The effects of urbanization and climate change impact to the flood risk of two governorates in Egypt were analyzed. Non-parametric change point and trend detection algorithms were applied to the annual rainfall, rainfall anomaly, and temperature anomaly of both study sites. Next, change points and trends of the annual and monthly surface runoff data generated by the Curve Number method over 1948-2014 were also analyzed to detect the effects of urbanization on the surface runoff. Lastly, a GIS decision support system was developed to delineate flood susceptibility zones for the two governorates. The significant decline in annual rainfall and rainfall anomaly after 1994 at 8.96 and 15.3 mm/decade respectively was likely due to climate change impact, especially significant warming trend since 1976 at 0.16 °C/decade, though that could partly be attributed to rapid urbanization. Since 1970, effects of urbanization to flood risk are clear, because despite a decline in rainfall, the annual surface runoff and runoff anomaly show positive trends of 12.7 and of 14.39 mm/decade, respectively. Eleven flood contributing factors have been identified and used in mapping flood susceptibility zones of both sites. In the El-Beheira governorate, 9.2%, 17.9%, 32.3%, 28.3% and 12.3% of its area are categorized as very high, high, moderate, low and very low susceptibility to flooding, respectively. Similarly, in Alexandria governorate, 15.9%, 33.5%, 41%, 8.8% and 0.8% of its area are categorized as very high, high, moderate, low and very low susceptibility to flooding, respectively. Very high and high susceptible zones are located in the northern, northwestern and northeastern parts of the Beheira governorates, and in the northeastern and northwestern parts of Alexandria. The flood related information obtained in this study will be useful to assist mitigating potential flood damages and future land use planning of both governorates of Egypt. Copyright © 2018 Elsevier B.V. All
Kulkarni, A. T.; Mohanty, J.; Eldho, T. I.; Rao, E. P.; Mohan, B. K.
Urban flooding has become an increasingly important issue in many parts of the world. In this study, an integrated flood assessment model (IFAM) is presented for the coastal urban flood simulation. A web based GIS framework has been adopted to organize the spatial datasets for the study area considered and to run the model within this framework. The integrated flood model consists of a mass balance based 1-D overland flow model, 1-D finite element based channel flow model based on diffusion wave approximation and a quasi 2-D raster flood inundation model based on the continuity equation. The model code is written in MATLAB and the application is integrated within a web GIS server product viz: Web Gram Server™ (WGS), developed at IIT Bombay, using Java, JSP and JQuery technologies. Its user interface is developed using open layers and the attribute data are stored in MySQL open source DBMS. The model is integrated within WGS and is called via Java script. The application has been demonstrated for two coastal urban watersheds of Navi Mumbai, India. Simulated flood extents for extreme rainfall event of 26 July, 2005 in the two urban watersheds of Navi Mumbai city are presented and discussed. The study demonstrates the effectiveness of the flood simulation tool in a web GIS environment to facilitate data access and visualization of GIS datasets and simulation results.
Foster, Kean; Bertacchi Uvo, Cintia; Olsson, Jonas
Hydropower makes up nearly half of Sweden's electrical energy production. However, the distribution of the water resources is not aligned with demand, as most of the inflows to the reservoirs occur during the spring flood period. This means that carefully planned reservoir management is required to help redistribute water resources to ensure optimal production and accurate forecasts of the spring flood volume (SFV) is essential for this. The current operational SFV forecasts use a historical ensemble approach where the HBV model is forced with historical observations of precipitation and temperature. In this work we develop and test a multi-model prototype, building on previous work, and evaluate its ability to forecast the SFV in 84 sub-basins in northern Sweden. The hypothesis explored in this work is that a multi-model seasonal forecast system incorporating different modelling approaches is generally more skilful at forecasting the SFV in snow dominated regions than a forecast system that utilises only one approach. The testing is done using cross-validated hindcasts for the period 1981-2015 and the results are evaluated against both climatology and the current system to determine skill. Both the multi-model methods considered showed skill over the reference forecasts. The version that combined the historical modelling chain, dynamical modelling chain, and statistical modelling chain performed better than the other and was chosen for the prototype. The prototype was able to outperform the current operational system 57 % of the time on average and reduce the error in the SFV by ˜ 6 % across all sub-basins and forecast dates.
Arnbjerg-Nielsen, Karsten; Zhou, Qianqian
uncertainty analysis, which can assess and quantify the overall uncertainty in relation to climate change adaptation to urban flash floods. The analysis is based on an uncertainty cascade that by means of Monte Carlo simulations of flood risk assessments incorporates climate change impacts as a key driver......There has been a significant increase in climatic extremes in many regions. In Central and Northern Europe, this has led to more frequent and more severe floods. Along with improved flood modelling technologies this has enabled development of economic assessment of climate change adaptation...... to increasing urban flood risk. Assessment of adaptation strategies often requires a comprehensive risk-based economic analysis of current risk, drivers of change of risk over time, and measures to reduce the risk. However, such studies are often associated with large uncertainties. The uncertainties arise from...
Wang, Ruo-Qian; Mao, Huina; Wang, Yuan; Rae, Chris; Shaw, Wesley
Hyper-resolution datasets for urban flooding are rare. This problem prevents detailed flooding risk analysis, urban flooding control, and the validation of hyper-resolution numerical models. We employed social media and crowdsourcing data to address this issue. Natural Language Processing and Computer Vision techniques are applied to the data collected from Twitter and MyCoast (a crowdsourcing app). We found these big data based flood monitoring approaches can complement the existing means of flood data collection. The extracted information is validated against precipitation data and road closure reports to examine the data quality. The two data collection approaches are compared and the two data mining methods are discussed. A series of suggestions is given to improve the data collection strategy.
Full Text Available In 2008, the seasonal forecast issued at the Seasonal Climate Outlook Forum for West Africa (PRESAO announced a high risk of above-normal rainfall for the July–September rainy season. With probabilities for above-normal rainfall of 0.45, this forecast indicated noteworthy increases in the risk of heavy rainfall. When this information reached the International Federation of Red Cross and Red Crescent Societies (IFRC West and Central Africa Office, it led to significant changes in the organization’s flood response operations. The IFRC regional office requested funds in advance of anticipated floods, prepositioned disaster relief items in strategic locations across West Africa to benefit up to 9,500 families, updated its flood contingency plans, and alerted vulnerable communities and decision-makers across the region. This forecast-based preparedness resulted in a decrease in the number of lives, property, and livelihoods lost to floods, compared to just one year prior in 2007 when similar floods claimed above 300 lives in the region. This article demonstrates how a science-based early warning informed decisions and saved lives by triggering action in anticipation of forecast events. It analyses what it took to move decision-makers to action, based on seasonal climate information, and to overcome traditional barriers to the uptake of seasonal climate information in the region, providing evidence that these barriers can be overcome. While some institutional, communication and technical barriers were addressed in 2008, many challenges remain. Scientists and humanitarians need to build more common ground.
E. Martínez Gomariz
Full Text Available Urban floods may provoke important damages to vehicles, usually not taken into account within most studies related to urban flood risks damage assessments. Herein a methodology to estimate damages to vehicles exposed to urban floods is presented. After a state-of-the-art review, the most recent damage curves for vehicles developed by the U.S. Army Corps of Engineers (USACE, 2009 are presented as the best adaptive and the most comprehensively performed so far. The proposed methodology is applied to the Spanish municipality of Badalona, framed in the H2020 European Project BINGO. In order to conduct this methodology some aspects such as the vehicular distribution are analyzed within the study area. Finally, Expected Annual Damage (EAD for flooded vehicles is calculated based on inundations related to design storms of different return periods (1, 10, 100 and 500 years.
Katiyar, N.; Hossain, F.
Floods have always been disastrous for human life. It accounts for about 15 % of the total death related to natural disasters. There are around 263 transboundary river basins listed by UNESCO, wherein at least 30 countries have more than 95% of their territory locked in one or more such transboundary basins. For flood forecasting in the lower riparian nations of these International River Basins (IRBs), real-time rainfall data from upstream nations is naturally the most critical factor governing the forecasting effectiveness. However, many upstream nations fail to provide data to the lower riparian nations due to a lack of in-situ rainfall measurement infrastructure or a lack of a treaty for real-time sharing of rainfall data. A potential solution is therefore to use satellites that inherently measure rainfall across political boundaries. NASA's proposed Global Precipitation Measurement (GPM) mission appears very promising in providing this vital rainfall information under the data- limited scenario that will continue to prevail in most IRBs. However, satellite rainfall is associated with uncertainty and hence, proper characterization of the satellite rainfall error propagation in hydrologic models for flood forecasting is a critical priority that should be resolved in the coming years in anticipation of GPM. In this study, we assess an open book modular watershed modeling approach for estimating the expected error in flood forecasting related to GPM rainfall data. Our motivation stems from the critical challenge in identifying the specific IRBs that would benefit from a pre-programmed satellite-based forecasting system in anticipation of GPM. As the number of flood-prone IRBs is large, conventional data-intensive implementation of existing physically-based distributed hydrologic models on case-by-case IRBs is considered time-consuming for completing such a global assessment. A more parsimonious approach is justified at the expense of a tolerable loss of detail and
Akyurek, Z.; Bozoğlu, B.; Sürer, S.; Mumcu, H.
In recent years, flooding has become an increasing concern across many parts of the world of both the general public and their governments. The climate change inducing more intense rainfall events occurring in short period of time lead flooding in rural and urban areas. In this study the flood modelling in an urbanized area, namely Samsun-Terme in Blacksea region of Turkey is performed. MIKE21 with flexible grid is used in 2-dimensional shallow water flow modelling. 1 × 1000-1 scaled maps with the buildings for the urbanized area and 1 × 5000-1 scaled maps for the rural parts are used to obtain DTM needed in the flood modelling. The bathymetry of the river is obtained from additional surveys. The main river passing through the urbanized area has a capacity of 500 m3 s-1 according to the design discharge obtained by simple ungauged discharge estimation depending on catchment area only. The upstream structural base precautions against flooding are modelled. The effect of four main upstream catchments on the flooding in the downstream urban area are modelled as different scenarios. It is observed that if the flow from the upstream catchments can be retarded through a detention pond constructed in one of the upstream catchments, estimated Q100 flood can be conveyed by the river without overtopping from the river channel. The operation of the upstream detention ponds and the scenarios to convey Q500 without causing flooding are also presented. Structural management measures to address changes in flood characteristics in water management planning are discussed.
Schubert, Jochen E.; Burns, Matthew J.; Fletcher, Tim D.; Sanders, Brett F.
This research outlines a framework for the case-specific assessment of Green Infrastructure (GI) performance in mitigating flood hazard in small urban catchments. The urban hydrologic modeling tool (MUSIC) is coupled with a fine resolution 2D hydrodynamic model (BreZo) to test to what extent retrofitting an urban watershed with GI, rainwater tanks and infiltration trenches in particular, can propagate flood management benefits downstream and support intuitive flood hazard maps useful for communicating and planning with communities. The hydrologic and hydraulic models are calibrated based on current catchment conditions, then modified to represent alternative GI scenarios including a complete lack of GI versus a full implementation of GI. Flow in the hydrologic/hydraulic models is forced using a range of synthetic rainfall events with annual exceedance probabilities (AEPs) between 1-63% and durations from 10 min to 24 h. Flood hazard benefits mapped by the framework include maximum flood depths and extents, flow intensity (m2/s), flood duration, and critical storm duration leading to maximum flood conditions. Application of the system to the Little Stringybark Creek (LSC) catchment shows that across the range of AEPs tested and for storm durations equal or less than 3 h, presently implemented GI reduces downstream flooded area on average by 29%, while a full implementation of GI would reduce downstream flooded area on average by 91%. A full implementation of GI could also lower maximum flow intensities by 83% on average, reducing the drowning hazard posed by urban streams and improving the potential for access by emergency responders. For storm durations longer than 3 h, a full implementation of GI lacks the capacity to retain the resulting rainfall depths and only reduces flooded area by 8% and flow intensity by 5.5%.
ten Veldhuis, J A E; Clemens, F H L R
In this study, three asset management strategies were compared with respect to their efficiency to reduce flood risk. Data from call centres at two municipalities were used to quantify urban flood risks associated with three causes of urban flooding: gully pot blockage, sewer pipe blockage and sewer overloading. The efficiency of three flood reduction strategies was assessed based on their effect on the causes contributing to flood risk. The sensitivity of the results to uncertainty in the data source, citizens' calls, was analysed through incorporation of uncertainty ranges taken from customer complaint literature. Based on the available data it could be shown that increasing gully pot blockage is the most efficient action to reduce flood risk, given data uncertainty. If differences between cause incidences are large, as in the presented case study, call data are sufficient to decide how flood risk can be most efficiently reduced. According to the results of this analysis, enlargement of sewer pipes is not an efficient strategy to reduce flood risk, because flood risk associated with sewer overloading is small compared to other failure mechanisms.
Seto, Karen C; Güneralp, Burak; Hutyra, Lucy R
Urban land-cover change threatens biodiversity and affects ecosystem productivity through loss of habitat, biomass, and carbon storage. However, despite projections that world urban populations will increase to nearly 5 billion by 2030, little is known about future locations, magnitudes, and rates of urban expansion. Here we develop spatially explicit probabilistic forecasts of global urban land-cover change and explore the direct impacts on biodiversity hotspots and tropical carbon biomass. If current trends in population density continue and all areas with high probabilities of urban expansion undergo change, then by 2030, urban land cover will increase by 1.2 million km(2), nearly tripling the global urban land area circa 2000. This increase would result in considerable loss of habitats in key biodiversity hotspots, with the highest rates of forecasted urban growth to take place in regions that were relatively undisturbed by urban development in 2000: the Eastern Afromontane, the Guinean Forests of West Africa, and the Western Ghats and Sri Lanka hotspots. Within the pan-tropics, loss in vegetation biomass from areas with high probability of urban expansion is estimated to be 1.38 PgC (0.05 PgC yr(-1)), equal to ∼5% of emissions from tropical deforestation and land-use change. Although urbanization is often considered a local issue, the aggregate global impacts of projected urban expansion will require significant policy changes to affect future growth trajectories to minimize global biodiversity and vegetation carbon losses.
Tkachenko, Nataliya; Procter, Rob; Jarvis, Stephen
This paper aims to explore whether there is a relationship between search patterns for flood risk information on the Web and how badly localities have been affected by flood events. We hypothesize that localities where people stay more actively informed about potential flooding experience less negative impact than localities where people make less effort to be informed. Being informed, of course, does not hold the waters back; however, it may stimulate (or serve as an indicator of) such resilient behaviours as timely use of sandbags, relocation of possessions from basements to upper floors and/or temporary evacuation from flooded homes to alternative accommodation. We make use of open data to test this relationship empirically. Our results demonstrate that although aggregated Web search reflects average rainfall patterns, its eigenvectors predominantly consist of locations with similar flood impacts during 2014-2015. These results are also consistent with statistically significant correlations of Web search eigenvectors with flood warning and incident reporting datasets.
Urban water flows are constitutive elements of Hanoi’s morphology. Regular floods across the city illustrate that Hanoi’s amphibious character is a central impediment to the installa- tion of a ‘dry and sanitary city’, the global modernist ideal of a separation of urban waste- water flows from
Li, Zhe; Yang, Dawen; Yang, Hanbo; Wu, Tianjiao; Xu, Jijun; Gao, Bing; Xu, Tao
The study area, the Three Gorges Region (TGR), plays a critical role in predicting the floods drained into the Three Gorges Reservoir, as reported local floods often exceed 10000m3/s during rainstorm events and trigger fast as well as significant impacts on the Three Gorges Reservoir's regulation. Meanwhile, it is one of typical mountainous areas in China, which is located in the transition zone between two monsoon systems: the East Asian monsoon and the South Asian (Indian) monsoon. This climatic feature, combined with local irregular terrains, has shaped complicated rainfall-runoff regimes in this focal region. However, due to the lack of high-resolution hydrometeorological data and physically-based hydrologic modeling framework, there was little knowledge about rainfall variability and flood pattern in this historically ungauged region, which posed great uncertainties to flash flood forecasting in the past. The present study summarize latest progresses of regional flash floods monitoring and prediction, including installation of a ground-based Hydrometeorological Observation Network (TGR-HMON), application of a regional geomorphology-based hydrological model (TGR-GBHM), development of an integrated forecasting and modeling system (TGR-INFORMS), and evaluation of quantitative precipitation estimations (QPE) and quantitative precipitation forecasting (QPF) products in TGR flash flood forecasting. With these continuing efforts to improve the forecasting performance of flash floods in TGR, we have addressed several critical issues: (1) Current observation network is still insufficient to capture localized rainstorms, and weather radar provides valuable information to forecast flash floods induced by localized rainstorms, although current radar QPE products can be improved substantially in future; (2) Long-term evaluation shows that the geomorphology-based distributed hydrologic model (GBHM) is able to simulate flash flooding processes reasonably, while model
Tanguy, M.; Chokmani, K.; Bernier, M.; Poulin, J.
When a flood affects an urban area, the managers and services responsible for public safety need precise and real time information on the localization of the flooded areas, on the submersion heights in those areas, but also on the vulnerability of people exposed to this hazard. Such information is essential for an effective crisis management. Despite a growing interest in this topic over the last 15 years, the development of flood risk assessment tools mainly focused on quantitative modeling of the monetary damages caused by floods to residential buildings or to critical infrastructures. Little attention was paid to the vulnerability of people exposed to flooding but also to the effects of the failure or destruction of critical infrastructures and residential building on people health and security during the disaster. Moreover, these models do not integrate the dynamic features of the flood (extent, submersion heights) and the evolution of human vulnerability in the same mapping tool. Thus, an accurate and precise evaluation of human risk induced by urban flooding is hardly feasible using such models. This study presents CADYRI, a dynamic mapping tool of human risk associated with flooding in urban areas, which fills the actual needs in terms of flood risk evaluation and management. This innovative tool integrates a methodology of flood hazard mapping that simulates, for a given discharge, the associated water level, and subsequently determines the extent of the flooded area and the submersion heights at each point of the flooded area, using a DEM. The dynamics of human vulnerability is then mapped at the household level, according to the characteristics of the flood hazard. Three key components of human vulnerability have been identified and are integrated to CADYRI: 1, the intrinsic vulnerability of the population, estimated by specific socio-economic indicators; 2, the vulnerability of buildings, assessed by their structural features; 3, the vulnerability of
Barbetta, Silvia; Coccia, Gabriele; Moramarco, Tommaso; Brocca, Luca; Todini, Ezio
This work extends the multi-temporal approach of the Model Conditional Processor (MCP-MT) to the multi-model case and to the four Truncated Normal Distributions (TNDs) approach, demonstrating the improvement on the single-temporal one. The study is framed in the context of probabilistic Bayesian decision-making that is appropriate to take rational decisions on uncertain future outcomes. As opposed to the direct use of deterministic forecasts, the probabilistic forecast identifies a predictive probability density function that represents a fundamental knowledge on future occurrences. The added value of MCP-MT is the identification of the probability that a critical situation will happen within the forecast lead-time and when, more likely, it will occur. MCP-MT is thoroughly tested for both single-model and multi-model configurations at a gauged site on the Tiber River, central Italy. The stages forecasted by two operative deterministic models, STAFOM-RCM and MISDc, are considered for the study. The dataset used for the analysis consists of hourly data from 34 flood events selected on a time series of six years. MCP-MT improves over the original models' forecasts: the peak overestimation and the rising limb delayed forecast, characterizing MISDc and STAFOM-RCM respectively, are significantly mitigated, with a reduced mean error on peak stage from 45 to 5 cm and an increased coefficient of persistence from 0.53 up to 0.75. The results show that MCP-MT outperforms the single-temporal approach and is potentially useful for supporting decision-making because the exceedance probability of hydrometric thresholds within a forecast horizon and the most probable flooding time can be estimated.
The study adopted multiple research approaches in selecting zones with both flood ... highlight the increasing evidence of negativity on houses and their values in terms of ... The potential for floods to wipeout housing wealth accumulated over ... In addition, locations do influence residential rental values through tangible or.
Hong Quan Nguyen
Full Text Available Water pollution associated with flooding is one of the major problems in cities in the global South. However, studies of water quality dynamics during flood events are not often reported in literature, probably due to difficult conditions for sampling during flood events. Water quality parameters in open water (canals, rivers, and lakes, flood water on roads and water in sewers have been monitored during the extreme fluvial flood event on 7 October 2013 in the city of Can Tho, Vietnam. This is the pioneering study of urban flood water pollution in real time in Vietnam. The results showed that water quality is very dynamic during flooding, especially at the beginning of the event. In addition, it was observed that the pathogen and contaminant levels in the flood water are almost as high as in sewers. The findings show that population exposed to flood water runs a health risk that is nearly equal to that of being in contact with sewer water. Therefore, the people of Can Tho not only face physical risk due to flooding, but are also exposed to health risks.
James W. N. Steenberg; Andrew A. Millward; David J. Nowak; Pamela J. Robinson; Alexis Ellis
The benefits derived from urban forest ecosystems are garnering increasing attention in ecological research and municipal planning. However, because of their location in heterogeneous and highly-altered urban landscapes, urban forests are vulnerable and commonly suffer disproportionate and varying levels of stress and disturbance. The objective of this study is to...
Urban flooding is often used as an illustration of the potentially adverse effects of greenhouse-induced climate change on extreme events. There is however, a paucity of studies that convert climate scenarios into changes in flood damage. This account summarises the use of modelling techniques, for three flood prone urban catchments in south eastern Australia, to assess changes to urban flood losses for the 'most wet' and ,most dry' scenarios for the year 2070. The most wet scenario indicates that annual average flood damage could increase within the range of 2.5 to 10 times, under the most dry scenario flood regimes would be similar to those experienced at present. The socio-economic scenarios based on the changes to flood losses are used to consider policy responses. It is unlikely that many local government authorities will respond because of lack of interest and because of major changes to the climate scenarios proposed over the last decade. Any response is likely to be incremental and accord with the 'no regrets' and 'the precautionary principle'. 21 refs
Full Text Available Issuing warning information to the public when rainfall exceeds given thresholds is a simple and widely-used method to minimize flood risk; however, this method lacks sophistication when compared with hydrodynamic simulation. In this study, an advanced methodology is proposed to improve the warning effectiveness of the rainfall threshold method for urban areas through deterministic-stochastic modeling, without sacrificing simplicity and efficiency. With regards to flooding mechanisms, rainfall thresholds of different durations are divided into two groups accounting for flooding caused by drainage overload and disastrous runoff, which help in grading the warning level in terms of emergency and severity when the two are observed together. A flood warning is then classified into four levels distinguished by green, yellow, orange, and red lights in ascending order of priority that indicate the required measures, from standby, flood defense, evacuation to rescue, respectively. The proposed methodology is tested according to 22 historical events in the last 10 years for 252 urbanized townships in Taiwan. The results show satisfactory accuracy in predicting the occurrence and timing of flooding, with a logical warning time series for taking progressive measures. For systems with multiple rainfall thresholds already in place, the methodology can be used to ensure better application of rainfall thresholds in urban flood warnings.
Full Text Available Flood processes and effects are examined, concerning two rivers in an urbanized area in North-Western Italy (Piedmont – Cuneo Plain. In May 2008, some areas in Northern Italy were struck by intense and persistent rainfall. In the Cuneo province (Southern Piedmont, floodplain with some urban areas was inundated over ca. ten square kilometres, and the city of Savigliano (about 21 000 inhabitants was particularly hit by flood. A purposely-made historical research has evidenced approximately fifty flood events as having occurred since 1350 in the Savigliano area. Based upon historical data, both documents and maps, GIS (Geographical Information System technique and field surveys were used to quantitatively assess the growing urbanization of the city and to describe flood processes and effects over years. This work aims to describe the dynamic behaviour of the 2008 flood, also comparing it to past events, in particular those that occurred in 1896. It is emphasized how the knowledge of past events can be helpful in reducing urban flooding.
Pedersen, Jonas Wied; Courdent, Vianney Augustin Thomas; Vezzaro, Luca
Numerical Weather Predictions (NWP) can be used to forecast urban runoff with long lead times. However, NWP exhibit large spatial uncertainties and using forecasted precipitation directly above the catchment might therefore not be an ideal approach in an online setup. We use the Danish...... Meteorological Institute’s NWP ensemble and investigate a large spatial neighborhood around the catchment over a two-year period. When compared against in-sewer observations, runoff forecasts forced with precipitation from north-east of the catchment are most skillful. This highlights spatial biases...
Yoshimura, Kouhei; Tajima, Yoshimitsu; Sanuki, Hiroshi; Shibuo, Yoshihiro; Sato, Shinji; Lee, SungAe; Furumai, Hiroaki; Koike, Toshio
Japan, with its natural mountainous landscape, has demographic feature that population is concentrated in lower reach of elevation close to the coast, and therefore flood damage with large socio-economic value tends to occur in low-lying region. Modeling of river flood in such low-lying urbanized river basin is complex due to the following reasons. In upstream it has been experienced urbanization, which changed land covers from natural forest or agricultural fields to residential or industrial area. Hence rate of infiltration and runoff are quite different from natural hydrological settings. In downstream, paved covers and construct of sewerage system in urbanized areas affect direct discharges and it enhances higher and faster flood peak arrival. Also tidal effect from river mouth strongly affects water levels in rivers, which must be taken into account. We develop an integrated river flood model in lower reach of urbanized areas to be able to address above described complex feature, by integrating model components: LSM coupled distributed hydrological model that models anthropogenic influence on river discharges to downstream; urban hydrological model that simulates run off response in urbanized areas; Saint Venant's equation approximated river model that integrates upstream and urban hydrological models with considering tidal effect from downstream. These features are integrated in a common modeling framework so that model interaction can be directly performed. The model is applied to the Tsurumi river basin, urbanized low-lying river basin in Yokohama and model results show that it can simulate water levels in rivers with acceptable model errors. Furthermore the model is able to install miscellaneous water planning constructs, such as runoff reduction pond in urbanized area, flood control field along the river channel, levee, etc. This can be a useful tool to investigate cost performance of hypothetical water management plan against impact of climate change in
Chou, Chien-Ming; Wang, Ru-Yih
This paper presents the application of a multimodel method using a wavelet-based Kalman filter (WKF) bank to simultaneously estimate decomposed state variables and unknown parameters for real-time flood forecasting. Applying the Haar wavelet transform alters the state vector and input vector of the state space. In this way, an overall detail plus approximation describes each new state vector and input vector, which allows the WKF to simultaneously estimate and decompose state variables. The wavelet-based multimodel Kalman filter (WMKF) is a multimodel Kalman filter (MKF), in which the Kalman filter has been substituted for a WKF. The WMKF then obtains M estimated state vectors. Next, the M state-estimates, each of which is weighted by its possibility that is also determined on-line, are combined to form an optimal estimate. Validations conducted for the Wu-Tu watershed, a small watershed in Taiwan, have demonstrated that the method is effective because of the decomposition of wavelet transform, the adaptation of the time-varying Kalman filter and the characteristics of the multimodel method. Validation results also reveal that the resulting method enhances the accuracy of the runoff prediction of the rainfall-runoff process in the Wu-Tu watershed.
Le Bihan, Guillaume; Payrastre, Olivier; Gaume, Eric; Moncoulon, David; Pons, Frédéric
Up to now, flash flood monitoring and forecasting systems, based on rainfall radar measurements and distributed rainfall-runoff models, generally aimed at estimating flood magnitudes - typically discharges or return periods - at selected river cross sections. The approach presented here goes one step further by proposing an integrated forecasting chain for the direct assessment of flash flood possible impacts on inhabited areas (number of buildings at risk in the presented case studies). The proposed approach includes, in addition to a distributed rainfall-runoff model, an automatic hydraulic method suited for the computation of flood extent maps on a dense river network and over large territories. The resulting catalogue of flood extent maps is then combined with land use data to build a flood impact curve for each considered river reach, i.e. the number of inundated buildings versus discharge. These curves are finally used to compute estimated impacts based on forecasted discharges. The approach has been extensively tested in the regions of Alès and Draguignan, located in the south of France, where well-documented major flash floods recently occurred. The article presents two types of validation results. First, the automatically computed flood extent maps and corresponding water levels are tested against rating curves at available river gauging stations as well as against local reference or observed flood extent maps. Second, a rich and comprehensive insurance claim database is used to evaluate the relevance of the estimated impacts for some recent major floods.
G. Le Bihan
Full Text Available Up to now, flash flood monitoring and forecasting systems, based on rainfall radar measurements and distributed rainfall–runoff models, generally aimed at estimating flood magnitudes – typically discharges or return periods – at selected river cross sections. The approach presented here goes one step further by proposing an integrated forecasting chain for the direct assessment of flash flood possible impacts on inhabited areas (number of buildings at risk in the presented case studies. The proposed approach includes, in addition to a distributed rainfall–runoff model, an automatic hydraulic method suited for the computation of flood extent maps on a dense river network and over large territories. The resulting catalogue of flood extent maps is then combined with land use data to build a flood impact curve for each considered river reach, i.e. the number of inundated buildings versus discharge. These curves are finally used to compute estimated impacts based on forecasted discharges. The approach has been extensively tested in the regions of Alès and Draguignan, located in the south of France, where well-documented major flash floods recently occurred. The article presents two types of validation results. First, the automatically computed flood extent maps and corresponding water levels are tested against rating curves at available river gauging stations as well as against local reference or observed flood extent maps. Second, a rich and comprehensive insurance claim database is used to evaluate the relevance of the estimated impacts for some recent major floods.
Miller, A. J.; Lee, G.; Bledsoe, B. P.; Stephens, T.
Small urban watersheds with high percent impervious cover and dense road and storm-drain networks are highly responsive to short-duration high-intensity rainfall events that lead to flash floods. The Baltimore metropolitan area has some of the flashiest urban watersheds in the conterminous U.S., high frequency of channel incision in affected areas, and a large number of watershed restoration projects designed to restore ecosystem services through reconnection of the channel with the floodplain. A question of key importance in these and other urban watersheds is to what extent we can mitigate flood hazards and urban stream syndrome through restoration activities that modify the channel and valley floor. Local and state governments have invested resources in repairing damage caused by extreme events like the July 30, 2016 Ellicott City flood in the Tiber River watershed, as well as more frequent high flows in other local urban streams. Recent reports have investigated how much flood mitigation may be achieved through modification of the channel and floodplain to enhance short-term storage of flood waters on the valley floor or in other subsurface structures, as compared with increasing stormwater management in the headwaters. Ongoing research conducted as part of the UWIN (Urban Water Innovation Network) program utilizes high-resolution topographic point clouds derived by processing of photographs from hand-held cameras or video frames from drone overflights. These are used both to track geomorphic change and to assess flood response with 2d hydraulic modeling tools under alternative mitigation scenarios. Assessment metrics include variations in inundation extent, water depth, hydrograph attenuation, and temporal and spatial characteristics of the 2d depth-averaged velocity field. Examples from diverse urban watersheds are presented to illustrate the range of anticipated outcomes and potential constraints on the effectiveness of downstream vs. headwater mitigation
Le Bihan, Guillaume; Payrastre, Olivier; Gaume, Eric; Pons, Frederic; Moncoulon, David
Hydrometeorological forecasting is an essential component of real-time flood management. The information it provides is of great help for crisis managers to anticipate the inundations and the associated risks. In the particular case of flash-floods, which may affect a large amount of small watersheds spread over the territory (up to 300 000 km of waterways considering a drained area of 5 km² minimum in France), appropriate flood forecasting systems are still under development. In France, highly distributed hydrological models have been implemented, enabling a real-time assessment of the potential intensity of flash-floods from the records of weather radars: AIGA-hydro system (Lavabre et al., 2005; Javelle et al., 2014), PreDiFlood project (Naulin et al., 2013). The approach presented here aims to go one step further by offering a direct assessment of the potential impacts of the simulated floods on inhabited areas. This approach is based on an a priori analysis of the study area in order (1) to evaluate with a simplified hydraulic approach (DTM treatment) the potentially flooded areas for different discharge levels, and (2) to identify the associated buildings and/or population at risk from geographic databases. This preliminary analysis enables to build an impact model (discharge-impact curve) on each river reach, which is then used to directly estimate the potentially affected assets based on a distributed rainfall runoff model. The overall principle of this approach was already presented at the 8th Hymex workshop. Therefore, the presentation will be here focused on the first validation results in terms of (1) accuracy of flooded areas simulated from DTM treatments, and (2) relevance of estimated impacts. The inundated areas simulated were compared to the European Directive cartography results (where available), showing an overall good correspondence in a large majority of cases, but also very significant errors for approximatively 10% of the river reaches
Löwe, Roland; Urich, Christian; Sto. Domingo, Niña Donna Farpale
the possibility to systematically test various flood risk adaptation measures ranging from large infrastructure changes over decentralised water management to urban planning policies. We have tested the framework in a case study in Melbourne, Australia considering 9 scenarios for urban development and climate......-off investments. Urban planning policies proved to be an efficient means for the reduction of flood risk, while implementing property buyback and pipe increases in a guideline-oriented manner was too costly. Random variations in location and time point of urban development could have significant impact on flood......We present a new framework for flexible testing of flood risk adaptation strategies in a variety of urban development and climate scenarios. This framework couples the 1D-2D hydrodynamic simulation package MIKE FLOOD with the agent-based urban development model DAnCE4Water and provides...
Full Text Available Sensitivity analysis is a fundamental approach to identify the most significant and sensitive parameters, helping us to understand complex hydrological models, particularly for time-consuming distributed flood forecasting models based on complicated theory with numerous parameters. Based on Sobol’ method, this study compared the sensitivity and interactions of distributed flood forecasting model parameters with and without accounting for correlation. Four objective functions: (1 Nash–Sutcliffe efficiency (ENS; (2 water balance coefficient (WB; (3 peak discharge efficiency (EP; and (4 time to peak efficiency (ETP were implemented to the Liuxihe model with hourly rainfall-runoff data collected in the Nanhua Creek catchment, Pearl River, China. Contrastive results for the sensitivity and interaction analysis were also illustrated among small, medium, and large flood magnitudes. Results demonstrated that the choice of objective functions had no effect on the sensitivity classification, while it had great influence on the sensitivity ranking for both uncorrelated and correlated cases. The Liuxihe model behaved and responded uniquely to various flood conditions. The results also indicated that the pairwise parameters interactions revealed a non-ignorable contribution to the model output variance. Parameters with high first or total order sensitivity indices presented a corresponding high second order sensitivity indices and correlation coefficients with other parameters. Without considering parameter correlations, the variance contributions of highly sensitive parameters might be underestimated and those of normally sensitive parameters might be overestimated. This research laid a basic foundation to improve the understanding of complex model behavior.
Yoon, Sunkwon; Jang, Sangmin; Park, Kyungwon
Extreme weather due to changing climate is a main source of water-related disasters such as flooding and inundation and its damage will be accelerated somewhere in world wide. To prevent the water-related disasters and mitigate their damage in urban areas in future, we developed a multi-sensor based real-time discharge forecasting system using remotely sensed data such as radar and satellite. We used Communication, Ocean and Meteorological Satellite (COMS) and Korea Meteorological Agency (KMA) weather radar for quantitative precipitation estimation. The Automatic Weather System (AWS) and McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) were used for verification of rainfall accuracy. The optimal Z-R relation was applied the Tropical Z-R relationship (Z=32R1.65), it has been confirmed that the accuracy is improved in the extreme rainfall events. In addition, the performance of blended multi-sensor combining rainfall was improved in 60mm/h rainfall and more strong heavy rainfall events. Moreover, we adjusted to forecast the urban discharge using Storm Water Management Model (SWMM). Several statistical methods have been used for assessment of model simulation between observed and simulated discharge. In terms of the correlation coefficient and r-squared discharge between observed and forecasted were highly correlated. Based on this study, we captured a possibility of real-time urban discharge forecasting system using remotely sensed data and its utilization for real-time flood warning. Acknowledgement This research was supported by a grant (13AWMP-B066744-01) from Advanced Water Management Research Program (AWMP) funded by Ministry of Land, Infrastructure and Transport (MOLIT) of Korean government.
Full Text Available An evolution of the urban waterlogging flood routing was studied in this paper based on the method of hexagonal grid modeling. Using the method of discrete grid, established an urban geometry model on account of the regular multi-scale discrete grid. With the fusion of 3D topographic survey data and 2D building vector data, formed a regular network model of surface. This model took multi special block into account, such as urban terrain and buildings. On this basis, a method of reverse flow deduction was proposed, which was an inverse computation from the state of flood to the evolution process. That is, based on the water depth of flood, made use of the connectivity with the outfall to calculate the range of water logging, and then implemented the urban waterlogging flood simulation deduction. The test indicated that, this method can implement the evolution of urban waterlogging scenario deduction effectively. And the correlational research could provide scientific basis for urban disaster prevention and emergency decision-making.
Full Text Available This paper presents an approach to enhance the role of local stakeholders in dealing with urban floods. The concept is based on the DIANE-CM project (Decentralised Integrated Analysis and Enhancement of Awareness through Collaborative Modelling and Management of Flood Risk of the 2nd ERANET CRUE funding initiative. The main objective of the project was to develop and test an advanced methodology for enhancing the resilience of local communities to flooding. Through collaborative modelling, a social learning process was initiated that enhances the social capacity of the stakeholders due to the interaction process. The other aim of the project was to better understand how data from hazard and vulnerability analyses and improved maps, as well as from the near real-time flood prediction, can be used to initiate a public dialogue (i.e. collaborative mapping and planning activities in order to carry out more informed and shared decision-making processes and to enhance flood risk awareness. The concept of collaborative modelling was applied in two case studies: (1 the Cranbrook catchment in the UK, with focus on pluvial flooding; and (2 the Alster catchment in Germany, with focus on fluvial flooding. As a result of the interactive and social learning process, supported by sociotechnical instruments, an understanding of flood risk was developed amongst the stakeholders and alternatives for flood risk management for the respective case study area were jointly developed and ranked as a basis for further planning and management.
Indrawan, I.; Siregar, R. I.
Based on the National Disaster Management Agency of Indonesia, the distribution of disasters and victims died until the year 2016 is the largest flood disaster. Deli River is a river that has the greatest flood potential through Medan City. In Deli Watershed, flow discharge affected by the discharge from its tributaries, the high rainfall intensity and human activity. We should anticipate reducing and preventing the occurrence of losses due to flood damage. One of the ways to anticipate flood disaster is to predict which part of urban area is would flood. The objective of this study is to analyze the flood inundation areas due to overflow of Deli River through Medan city. Two-dimensional modeling by HEC-RAS 5.0.3 is a widely used hydraulic software tool developed by the U.S Army Corps of Engineers, which combined with the HEC-HMS for hydrological modeling. The result shows flood vulnerability in Medan by a map to present the spot that vulnerable about flood. The flooded area due to the overflowing of Deli River consists of seven sub districts, namely Medan Johor, Medan Selayang, Medan Kota, Medan Petisah, Medan Maimun, Medan Perjuangan and Medan Barat.
Rafiu O. Salami
Full Text Available In the recent past, the frequency and gravity of large-scale flood disasters have increased globally, resulting in casualties, destruction of property and huge economic loss. The destructive flood disaster devastating Louisiana, USA, is a recent example. Despite the availability of advanced technological capabilities for dealing with floods in developed nations, flood disasters continue to become more rampant and disastrous. Developing countries in Africa such as Benin, Ghana, Nigeria, Senegal and Sudan have recently experienced severe flooding, leaving a considerable number of human casualties and thousands displaced. In African cities, most vulnerable urban residents usually have lesser capacity and fewer resources to recover from the shocks of disaster as a result of the failure of governments to build human security for poor African residents. Many scholars have acknowledged the lack of appropriate vulnerability assessment frameworks and policies, questioning the efficiency and effectiveness of the tested models in Africa. The ability to accurately identify, measure and evaluate the various vulnerabilities of affected people and communities is a right step towards reducing disaster risk. This article aimed at developing a framework for assessing urban settlements’ vulnerability to flood risks in Africa. The framework is currently being tested to assess various dimensions of vulnerability drivers in three urban communities in Ibadan metropolis, the third largest city in Nigeria, focusing more on flood risk perceptions and behaviour of the risk bearers. It uses participatory and mixed method approaches to socially construct vulnerability of populations at risk. This model emanates from the evaluation of considerable relevant literature and an array of vulnerability assessment frameworks. It integrates some approaches that are applicable to African cities in a bid to create a versatile tool to assess, identify and mitigate the effects of
Zakaria, Siti Fairus; Zin, Rosli Mohamad; Mohamad, Ismail; Balubaid, Saeed; Mydin, Shaik Hussein; MDR, E. M. Roodienyanto
In Malaysia, flash floods are common occurrences throughout the year in flood prone areas. In terms of flood extent, flash floods affect smaller areas but because of its tendency to occur in densely urbanized areas, the value of damaged property is high and disruption to traffic flow and businesses are substantial. However, in river floods especially the river floods of Kelantan and Pahang, the flood extent is widespread and can extend over 1,000 square kilometers. Although the value of property and density of affected population is lower, the damage inflicted by these floods can also be high because the area affected is large. In order to combat these floods, various flood mitigation measures have been carried out. Structural flood mitigation alone can only provide protection levels from 10 to 100 years Average Recurrence Intervals (ARI). One of the economically effective non-structural approaches in flood mitigation and flood management is using a geospatial technology which involves flood forecasting and warning services to the flood prone areas. This approach which involves the use of Geographical Information Flood Forecasting system also includes the generation of a series of flood maps. There are three types of flood maps namely Flood Hazard Map, Flood Risk Map and Flood Evacuation Map. Flood Hazard Map is used to determine areas susceptible to flooding when discharge from a stream exceeds the bank-full stage. Early warnings of incoming flood events will enable the flood victims to prepare themselves before flooding occurs. Properties and life's can be saved by keeping their movable properties above the flood levels and if necessary, an early evacuation from the area. With respect to flood fighting, an early warning with reference through a series of flood maps including flood hazard map, flood risk map and flood evacuation map of the approaching flood should be able to alert the organization in charge of the flood fighting actions and the authority to
Kaspersen, Per Skougaard
Urban areas are characterized by very high concentrations of people and economic activities and are thus particularly vulnerable to flooding dur ing extreme precipitation. Urban development and climate change are among the key drivers of changes in the exposure of cities to the occurrence...... and impacts of pluvial flooding. Cities are often dominated by large areas of impervious surfaces, that is, man-made sealed surfaces which water cannot penetrate, and increases in these – for example, as a consequence of urban development – can cause elevated run-off volumes and flood levels during...... precipitation. Climate change is expected to affect the intensity and frequency of extreme precipitation, with increases projected for many regions, including most parts of Europe....
Skovgård Olsen, Anders; Zhou, Qianqian; Linde, Jens Jørgen
Estimating the expected annual damage (EAD) due to flooding in an urban area is of great interest for urban water managers and other stakeholders. It is a strong indicator for a given area showing how vulnerable it is to flood risk and how much can be gained by implementing e.g., climate change...... adaptation measures. This study identifies and compares three different methods for estimating the EAD based on unit costs of flooding of urban assets. One of these methods was used in previous studies and calculates the EAD based on a few extreme events by assuming a log-linear relationship between cost...... of an event and the corresponding return period. This method is compared to methods that are either more complicated or require more calculations. The choice of method by which the EAD is calculated appears to be of minor importance. At all three case study areas it seems more important that there is a shift...
Huang, Chen-Jia; Hsu, Ming-hsi; Teng, Wei-Hsien; Lin, Tsung-Hsien
Typhoons always induce heavy rainfall during summer and autumn seasons in Taiwan. Extreme weather in recent years often causes severe flooding which result in serious losses of life and property. With the rapid industrial and commercial development, people care about not only the quality of life, but also the safety of life and property. So the impact of life and property due to disaster is the most serious problem concerned by the residents. For the mitigation of the disaster impact, the flood hazard and risk analysis play an important role for the disaster prevention and mitigation. In this study, the vulnerability of Kaohsiung city was evaluated by statistics of social development factor. The hazard factors of Kaohsiung city was calculated by simulated flood depth of six different return periods and four typhoon events which result in serious flooding in Kaohsiung city. The flood risk can be obtained by means of the flood hazard and social vulnerability. The analysis results provide authority to strengthen disaster preparedness and to set up more resources in high risk areas.
M. W. Rotach
Full Text Available D-PHASE was a Forecast Demonstration Project of the World Weather Research Programme (WWRP related to the Mesoscale Alpine Programme (MAP. Its goal was to demonstrate the reliability and quality of operational forecasting of orographically influenced (determined precipitation in the Alps and its consequences on the distribution of run-off characteristics. A special focus was, of course, on heavy-precipitation events.
The D-PHASE Operations Period (DOP ran from June to November~2007, during which an end-to-end forecasting system was operated covering many individual catchments in the Alps, with their water authorities, civil protection organizations or other end users. The forecasting system's core piece was a Visualization Platform where precipitation and flood warnings from some 30 atmospheric and 7 hydrological models (both deterministic and probabilistic and corresponding model fields were displayed in uniform and comparable formats. Also, meteograms, nowcasting information and end user communication was made available to all the forecasters, users and end users. D-PHASE information was assessed and used by some 50 different groups ranging from atmospheric forecasters to civil protection authorities or water management bodies.
In the present contribution, D-PHASE is briefly presented along with its outstanding scientific results and, in particular, the lessons learnt with respect to uncertainty propagation. A focus is thereby on the transfer of ensemble prediction information into the hydrological community and its use with respect to other aspects of societal impact. Objective verification of forecast quality is contrasted to subjective quality assessments during the project (end user workshops, questionnaires and some general conclusions concerning forecast demonstration projects are drawn.
see their already considerable vulnerability increased for every flooding event. In the long term, climate change is expected to make matters worse for these already tried populations, due to an increase in storm frequency and intensity, and with them in the risk of floods. However, climate change......-induced changing weather patterns and more extreme weather events are only part of the explanation for this situation, as large segments of the urban population in West Africa are not offered the public services, infrastructure and protective regulations needed in order to respond to floods. In Senegal, in spite...
Freni, G; La Loggia, G; Notaro, V
Due to the increased occurrence of flooding events in urban areas, many procedures for flood damage quantification have been defined in recent decades. The lack of large databases in most cases is overcome by combining the output of urban drainage models and damage curves linking flooding to expected damage. The application of advanced hydraulic models as diagnostic, design and decision-making support tools has become a standard practice in hydraulic research and application. Flooding damage functions are usually evaluated by a priori estimation of potential damage (based on the value of exposed goods) or by interpolating real damage data (recorded during historical flooding events). Hydraulic models have undergone continuous advancements, pushed forward by increasing computer capacity. The details of the flooding propagation process on the surface and the details of the interconnections between underground and surface drainage systems have been studied extensively in recent years, resulting in progressively more reliable models. The same level of was advancement has not been reached with regard to damage curves, for which improvements are highly connected to data availability; this remains the main bottleneck in the expected flooding damage estimation. Such functions are usually affected by significant uncertainty intrinsically related to the collected data and to the simplified structure of the adopted functional relationships. The present paper aimed to evaluate this uncertainty by comparing the intrinsic uncertainty connected to the construction of the damage-depth function to the hydraulic model uncertainty. In this way, the paper sought to evaluate the role of hydraulic model detail level in the wider context of flood damage estimation. This paper demonstrated that the use of detailed hydraulic models might not be justified because of the higher computational cost and the significant uncertainty in damage estimation curves. This uncertainty occurs mainly
Bai, Yun; Wang, Pu; Li, Chuan; Xie, Jingjing; Wang, Yin
Water is one of the most important resources for economic and social developments. Daily water demand forecasting is an effective measure for scheduling urban water facilities. This work proposes a multi-scale relevance vector regression (MSRVR) approach to forecast daily urban water demand. The approach uses the stationary wavelet transform to decompose historical time series of daily water supplies into different scales. At each scale, the wavelet coefficients are used to train a machine-learning model using the relevance vector regression (RVR) method. The estimated coefficients of the RVR outputs for all of the scales are employed to reconstruct the forecasting result through the inverse wavelet transform. To better facilitate the MSRVR forecasting, the chaos features of the daily water supply series are analyzed to determine the input variables of the RVR model. In addition, an adaptive chaos particle swarm optimization algorithm is used to find the optimal combination of the RVR model parameters. The MSRVR approach is evaluated using real data collected from two waterworks and is compared with recently reported methods. The results show that the proposed MSRVR method can forecast daily urban water demand much more precisely in terms of the normalized root-mean-square error, correlation coefficient, and mean absolute percentage error criteria.
Martin, A.; Ralph, F. M.; Lavers, D. A.; Kalansky, J.; Kawzenuk, B.
The previous ten years has seen an explosion in research devoted to the Atmospheric River (AR) phenomena, features of the midlatitude circulation responsible for large horizontal water vapor transport. Upon landfall, ARs can be associated with 30-50% of annual precipitation in some regions, while also causing the largest flooding events in places such as coastal California. Little discussed is the role secondary frontal waves play in modulating precipitation during a landfalling AR. Secondary frontal waves develop along an existing cold front in response to baroclinic frontogenesis, often coinciding with a strong upper-tropospheric jet. If the secondary wave develops along a front associated with a landfalling AR, the resulting precipitation may be much greater or much less than originally forecasted - especially in regions where orographic uplift of horizontally transported water vapor is responsible for a large portion of precipitation. In this study, we present several cases of secondary frontal waves that have occurred in conjunction with a landfalling AR on the US West Coast. We put the impact of these cases in historical perspective using quantitative precipitation forecasts, satellite data, reanalyses, and estimates of damage related to flooding. We also discuss the dynamical mechanisms behind secondary frontal wave development and relate these mechanisms to the high spatiotemporal variability in precipitation observed during ARs with secondary frontal waves. Finally, we demonstrate that even at lead times less than 24 hours, current quantitative precipitation forecasting methods have difficulty accurately predicting the rainfall in the area near the secondary wave landfall, in some cases leading to missed or false alarm flood warnings, and suggest methods which may improve quantitative precipitation forecasts for this type of system in the future.
Liu, Y.; Liu, Q.; Fan, W.; Wang, G.
In this paper, satellite image, remote sensing technique and geographic information system technique are main technical bases. Spectral and other factors comprehensive analysis and visual interpretation are main methods. We use GF-1 and Landsat8 remote sensing satellite image of Wuhan as data source, and from which we extract vegetation distribution, urban heat island relative intensity distribution map and urban flood submergence range. Based on the extracted information, through spatial analysis and regression analysis, we find correlations among heat island effect, vegetation coverage and urban flood. The results show that there is a high degree of overlap between of urban heat island and urban flood. The area of urban heat island has buildings with little vegetation cover, which may be one of the reasons for the local heavy rainstorms. Furthermore, the urban heat island has a negative correlation with vegetation coverage, and the heat island effect can be alleviated by the vegetation to a certain extent. So it is easy to understand that the new industrial zones and commercial areas which under constructions distribute in the city, these land surfaces becoming bare or have low vegetation coverage, can form new heat islands easily.
Meesuk, V.; Vojinovic, Zoran; Mynett, A.E.; Piasecki, M
Top-view data obtainedfrom LiDAR systemshas long been used as topographic-input data for urban flood modelling applications. This high-resolution input data has considerable potential to improve urban flood modelling predictions with more detail. However, the difficulty of employing top-view data is
Siregar, R. I.
The upper Citarum River watershed utilizes remote sensing technology in Geographic Information System to provide information on land coverage by interpretation of objects in the image. Rivers that pass through urban areas will cause flooding problems causing disadvantages, and it disrupts community activities in the urban area. Increased development in a city is related to an increase in the number of population growth that added by increasing quality and quantity of life necessities. Improved urban lifestyle changes have an impact on land cover. The impact in over time will be difficult to control. This study aims to analyze the condition of flooding in urban areas caused by upper Citarum watershed land-use change in 2001 with the land cover change in 2010. This modeling analyzes with the help of HEC-RAS to describe flooded inundation urban areas. Land cover change in upper Citarum watershed is not very significant; it based on the results of data processing of land cover has the difference of area that changed is not enormous. Land cover changes for the floods increased dramatically to a flow coefficient for 2001 is 0.65 and in 2010 at 0.69. In 2001, the inundation area about 105,468 hectares and it were about 92,289 hectares in 2010.
Sun, Pingping; Wang, Shuqian; Gan, Hong; Liu, Bin; Jia, Ling
Flooding in small and medium rivers are seriously threatening the safety of human beings’ life and property. The simulation forecasting of the river flood and bank risk in hilly region has gradually become a hotspot. At present, there are few studies on the simulation of hilly perched river, especially in the case of lacking section flow data. And the method of how to determine the position of the levee breach along the river bank is not much enough. Based on the characteristics of the sections in hilly perched river, an attempt is applied in this paper which establishes the correlation between the flow profile computed by HEC-RAS model and the river bank. A hilly perched river in Lingshi County, Shanxi Province of China, is taken as the study object, the levee breach positions along the bank are simulated under four different design storm. The results show that the flood control standard of upper reach is high, which can withstand the design storm of 100 years. The current standard of lower reach is low, which is the flooding channel with high frequency. As the standard of current channel between the 2rd and the 11th section is low, levee along that channel of the river bank is considered to be heighten and reinforced. The study results can provide some technical support for flood proofing in hilly region and some reference for the reinforcement of river bank.
Park, Shinju; Berenguer, Marc; Sempere-Torres, Daniel; Baugh, Calum; Smith, Paul
Flash floods induced by heavy rain are one of the hazardous natural events that significantly affect human lives. Because flash floods are characterized by their rapid onset, forecasting flash flood to lead an effective response requires accurate rainfall predictions with high spatial and temporal resolution and adequate representation of the hydrologic and hydraulic processes within a catchment that determine rainfall-runoff accumulations. We present extreme flash flood cases which occurred throughout Europe in 2015-2016 that were identified and forecasted by two real-time approaches: 1) the European Rainfall-Induced Hazard Assessment System (ERICHA) and 2) the European Runoff Index based on Climatology (ERIC). ERICHA is based on the nowcasts of accumulated precipitation generated from the pan-European radar composites produced by the EUMETNET project OPERA. It has the advantage of high-resolution precipitation inputs and rapidly updated forecasts (every 15 minutes), but limited forecast lead time (up to 8 hours). ERIC, on the other hand, provides 5-day forecasts based on the COSMO-LEPS NWP simulations updated 2 times a day but is only produced at a 7 km resolution. We compare the products from both systems and focus on showing the advantages, limitations and complementarities of ERICHA and ERIC for seamless high-resolution flash flood forecasting.
Full Text Available The aim of the presented study was to assess the distribution of flood-risk potential (FRP at the regional scale. A progressive approach integrating geographical information system (GIS with two different methods of multicriteria analysis (MCA – analytic hierarchy process (AHP and ranking method (RM was applied in the process. In the analyses, the most causative factors for flooding were taken into account, urban spatial planning, such as soil type, daily precipitation, land use, size of the catchment and average basin slope. A case study of flood vulnerability identification in the Hornád and Bodrog catchments’ areas in eastern Slovakia has been employed to illustrate two different approaches. Spatial estimation of FRP should be one of the basic steps for complex geoecological evaluation and delimitation of landscape considering water resources management, groundwater pollution, prediction of soil erosion and sediment transport and some other important landscape-ecological factors. The obtained results indicate that RM method shows better results as related to the existing floods in the recent years in Bodrog and Hornád catchment than AHP method.
According to the National Weather Service, more than : half of the fatalities attributed to flash floods are : people swept away in vehicles when trying to cross an : intersection that is flooded. Efforts are underway to : improve prediction of the l...
Ward, Philip J.; Jongman, Brenden; Aerts, Jeroen C. J. H.; Bates, Paul D.; Botzen, Wouter J. W.; Diaz Loaiza, Andres; Hallegatte, Stephane; Kind, Jarl M.; Kwadijk, Jaap; Scussolini, Paolo; Winsemius, Hessel C.
Floods cause billions of dollars of damage each year, and flood risks are expected to increase due to socio-economic development, subsidence, and climate change. Implementing additional flood risk management measures can limit losses, protecting people and livelihoods. Whilst several models have been developed to assess global-scale river-flood risk, methods for evaluating flood risk management investments globally are lacking. Here, we present a framework for assessing costs and benefits of structural flood protection measures in urban areas around the world. We demonstrate its use under different assumptions of current and future climate change and socio-economic development. Under these assumptions, investments in dykes may be economically attractive for reducing risk in large parts of the world, but not everywhere. In some regions, economically efficient investments could reduce future flood risk below today’s levels, in spite of climate change and economic growth. We also demonstrate the sensitivity of the results to different assumptions and parameters. The framework can be used to identify regions where river-flood protection investments should be prioritized, or where other risk-reducing strategies should be emphasized.
Quantitative analysis of the risk for reservoir real-time operation is a hard task owing to the difficulty of accurate description of inflow uncertainties. The ensemble-based hydrologic forecasts directly depict the inflows not only the marginal distributions but also their persistence via scenarios. This motivates us to analyze the reservoir real-time operating risk with ensemble-based hydrologic forecasts as inputs. A method is developed by using the forecast horizon point to divide the future time into two stages, the forecast lead-time and the unpredicted time. The risk within the forecast lead-time is computed based on counting the failure number of forecast scenarios, and the risk in the unpredicted time is estimated using reservoir routing with the design floods and the reservoir water levels of forecast horizon point. As a result, a two-stage risk analysis method is set up to quantify the entire flood risks by defining the ratio of the number of scenarios that excessive the critical value to the total number of scenarios. The China's Three Gorges Reservoir (TGR) is selected as a case study, where the parameter and precipitation uncertainties are implemented to produce ensemble-based hydrologic forecasts. The Bayesian inference, Markov Chain Monte Carlo, is used to account for the parameter uncertainty. Two reservoir operation schemes, the real operated and scenario optimization, are evaluated for the flood risks and hydropower profits analysis. With the 2010 flood, it is found that the improvement of the hydrologic forecast accuracy is unnecessary to decrease the reservoir real-time operation risk, and most risks are from the forecast lead-time. It is therefore valuable to decrease the avarice of ensemble-based hydrologic forecasts with less bias for a reservoir operational purpose.
Krauße, T.; Cullmann, J.
validated for flood forecasting in a small catchment characterised by extreme process dynamics.
Rossa, Andrea M.; Laudanna Del Guerra, Franco; Borga, Marco; Zanon, Francesco; Settin, Tommaso; Leuenberger, Daniel
SummaryThis study aims to assess the feasibility of assimilating carefully checked radar rainfall estimates into a numerical weather prediction (NWP) to extend the forecasting lead time for an extreme flash flood. The hydro-meteorological modeling chain includes the convection-permitting NWP model COSMO-2 and a coupled hydrological-hydraulic model. Radar rainfall estimates are assimilated into the NWP model via the latent heat nudging method. The study is focused on 26 September 2007 extreme flash flood which impacted the coastal area of North-eastern Italy around Venice. The hydro-meteorological modeling system is implemented over the 90 km2 Dese river basin draining to the Venice Lagoon. The radar rainfall observations are carefully checked for artifacts, including rain-induced signal attenuation, by means of physics-based correction procedures and comparison with a dense network of raingauges. The impact of the radar rainfall estimates in the assimilation cycle of the NWP model is very significant. The main individual organized convective systems are successfully introduced into the model state, both in terms of timing and localization. Also, high-intensity incorrectly localized precipitation is correctly reduced to about the observed levels. On the other hand, the highest rainfall intensities computed after assimilation underestimate the observed values by 20% and 50% at a scale of 20 km and 5 km, respectively. The positive impact of assimilating radar rainfall estimates is carried over into the free forecast for about 2-5 h, depending on when the forecast was started. The positive impact is larger when the main mesoscale convective system is present in the initial conditions. The improvements in the precipitation forecasts are propagated to the river flow simulations, with an extension of the forecasting lead time up to 3 h.
Full Text Available Atmospheric pollutants concentration forecasting is an important issue in air quality monitoring. Qualitair Corse, the organization responsible for monitoring air quality in Corsica (France, needs to develop a short-term prediction model to lead its mission of information towards the public. Various deterministic models exist for local forecasting, but need important computing resources, a good knowledge of atmospheric processes and can be inaccurate because of local climatical or geographical particularities, as observed in Corsica, a mountainous island located in the Mediterranean Sea. As a result, we focus in this study on statistical models, and particularly Artificial Neural Networks (ANNs that have shown good results in the prediction of ozone concentration one hour ahead with data measured locally. The purpose of this study is to build a predictor realizing predictions of ozone 24 hours ahead in Corsica in order to be able to anticipate pollution peaks formation and to take appropriate preventive measures. Specific meteorological conditions are known to lead to particular pollution event in Corsica (e.g. Saharan dust events. Therefore, an ANN model will be used with pollutant and meteorological data for operational forecasting. Index of agreement of this model was calculated with a one year test dataset and reached 0.88.
Full Text Available A key reason why some ecosystem services are undervalued is because they are not easily perceived both by beneficiaries and potential providers. Hydrological modeling allows us to assess, quantify, and visualize the causal link between a particular human intervention and the positive or negative impacts this has on flooding. This study uses such a model to test hypothetical changes in land use in the Brazilian coastal city of Paraty. We discuss how the adoption of higher density patterns of urban development can respond to the needs of a growing population, while safeguarding cultural landscapes of high environmental value against unsustainable urban sprawl and encroachment. Results of the modeling exercise show how water-flow regulation services can be improved, and to what extent restoring natural functions and properties of peri-urban floodplains may reduce urban flooding.
Turner-Gillespie, Daniel F.; Smith, James A.; Bates, Paul D.
The Charlotte, North Carolina metropolitan area has experienced extensive urban and suburban growth and sharply increasing trends in the magnitude and frequency of flooding. The hydraulics and hydrology of flood response in the region are examined through a combination of numerical modeling studies and diagnostic analyses of paired discharge observations from upstream-downstream gaging stations. The regional flood response is shown to strongly reflect urbanization effects, which increase flood peaks and decrease response times, and geologically controlled attenuating reaches, which decrease flood peaks and increase lag times. Attenuating reaches are characterized by systematic changes in valley bottom geometry and longitudinal profile. The morphology of the fluvial system is controlled by the bedrock geology, with pronounced changes occurring at or near contacts between intrusive igneous and metamorphic rocks. Analyses of wave celerity and flood peak attenuation over a range of discharge values for an 8.3 km valley bottom section of Little Sugar Creek are consistent with Knight and Shiono's characterization of the variation of flood wave velocity from in-channel conditions to valley bottom full conditions. The cumulative effect of variation in longitudinal profile, expansions and contractions of the valley bottom, floodplain roughness and sub-basin flood response is investigated using a two-dimensional, depth-averaged, finite element hydrodynamic model coupled with a distributed hydrologic model. For a 10.1 km stream reach of Briar Creek, with drainage area ranging from 13 km 2 at the upstream end of the reach to 49 km 2 at the downstream end, it is shown that flood response reflects a complex interplay of hydrologic and hydraulic processes on hillslopes and valley bottoms.
Marcelo Gomes Miguez
Full Text Available The development of cities has always had a very close relation with water. However, cities directly impact land use patterns and greatly change natural landscapes, aggravating floods. Considering this situation, this paper intends to discuss lowland occupation and city sustainability in what regards urban stormwater management, fluvial space, and river restoration, aiming at minimizing flood risks and improving natural and built environment conditions. River plains tend to be attractive places for a city to grow. From ancient times, levees have been used to protect lowland areas along major watercourses to allow their occupation. However, urban rivers demand space for temporary flood storage. From a systemic point of view, levees along extensive river reaches act as canalization works, limiting river connectivity with flood plains, rising water levels, increasing overtopping risks and transferring floods downstream. Departing from this discussion, four case studies in the Iguaçu-Sarapuí River Basin, a lowland area of Rio de Janeiro State, Brazil, are used to compose a perspective in which the central point refers to the need of respecting watershed limits and giving space to rivers. Different aspects of low-lying city planning are discussed and analyzed concerning the integration of the built and natural environments.
Urban water flows are constitutive elements of Hanoi’s morphology. Regular floods across the city illustrate that Hanoi’s amphibious character is a central impediment to the installa- tion of a ‘dry and sanitary city’, the global modernist ideal of a separation of urban waste- water flows from public space through their redirection into large underground networks. Currently, the first attempt by the city government to construct a citywide sewerage network since the colonial period is taking p...
Gronewold, A.; Seglenieks, F.; Bruxer, J.; Fortin, V.; Noel, J.
In the spring of 2017, water levels across Lake Ontario and the upper St. Lawrence River exceeded record high levels, leading to widespread flooding, damage to property, and controversy over regional dam operating protocols. Only a few years earlier, water levels on Lakes Superior, Michigan, and Huron (upstream of Lake Ontario) had dropped to record low levels leading to speculation that either anthropogenic controls or climate change were leading to chronic water loss from the Great Lakes. The contrast between low water level conditions across Earth's largest lake system from the late 1990s through 2013, and the rapid rise prior to the flooding in early 2017, underscores the challenges of quantifying and forecasting hydrologic impacts of rising regional air and water temperatures (and associated changes in lake evaporation) and persistent increases in long-term precipitation. Here, we assess the hydrologic conditions leading to the recent record flooding across the Lake Ontario - St. Lawrence River system, with a particular emphasis on understanding the extent to which those conditions were consistent with observed and anticipated changes in historical and future climate, and the extent to which those conditions could have been anticipated through improvements in seasonal climate outlooks and hydrological forecasts.
Tellez Alvarez, Jackson David; Gomez, Manuel; Russo, Beniamino; Redondo, Jose M.
One of the most important problems that have some cities is the urban floods because of poor drainage design. Therefore the systems the drainage do not have the capacity of capture the flow of discharge generated in a rain event and insert it into the drainage network. Even though the two problems that have caught the main attention are the evaluation of the volumes falling in the river basin because extreme rainfall events often lead to urban pluvial flooding being a hydrologic problem and the hydraulic design of the sewer network being a hydraulic problem to limiting capacity of the drainage system, there is an intermediate step between these two processes that is necessary to solve that is the hydraulic behavior of the grate inlet. We need to collect the runoff produced on the city surface and to introduce it in the sewer network. Normally foundry companies provide complete information about drainage grate structural capacity but provide nothing about their hydraulic capacity. This fact can be seen because at the moment does not exist any official regulation at national or international level in this field. It's obvious that, nowadays, there is a great gap in this field at the legislative level owing to the complexity of this field and the modernity of the urban hydrology as science . In essence, we shows the relevance to know the inlet hydraulic interception capacity because surface drainage requires a satisfactory knowledge on storm frequency, gutter flow and above all inlet capacity. In addition, we development an important achievement is the invention and development of techniques for measurement of field velocities in hydraulics engineering applications. Hence knowledge the technological advances in digital cameras with high resolution and high speed found in the environmental, and the advances in image processing techniques, therefore now is a tremendous potential to obtain of behavior of the water surface flow . A novel technique using particle
Among the many impacts noted, flooding has appeared recently as a major threat for poor population leaving in the suburbs of Dakar. This study carried out at the outskirts of the town, in Yeumbeul District (17°24' North, 14°46' West), tries from rainfall variability, Digital Terrain Model and land cover change analysis since ...
Full Text Available Cities worldwide are challenged by increasing urban flood risks. Precise and realistic measures are required to reduce flooding impacts. However, currently implemented sewer and topographic models do not provide realistic predictions of local flooding occurrence during heavy rain events. Assessing other factors such as spatially distributed rainfall, socioeconomic characteristics, and social sensing, may help to explain probability and impacts of urban flooding. Several spatial datasets have been recently made available in the Netherlands, including rainfall-related incident reports made by citizens, spatially distributed rain depths, semidistributed socioeconomic information, and buildings age. Inspecting the potential of this data to explain the occurrence of rainfall related incidents has not been done yet. Multivariate analysis tools for describing communities and environmental patterns have been previously developed and used in the field of study of ecology. The objective of this paper is to outline opportunities for these tools to explore urban flooding risks patterns in the mentioned datasets. To that end, a cluster analysis is performed. Results indicate that incidence of rainfall-related impacts is higher in areas characterized by older infrastructure and higher population density.
Gaitan, S.; ten Veldhuis, J. A. E.
Cities worldwide are challenged by increasing urban flood risks. Precise and realistic measures are required to reduce flooding impacts. However, currently implemented sewer and topographic models do not provide realistic predictions of local flooding occurrence during heavy rain events. Assessing other factors such as spatially distributed rainfall, socioeconomic characteristics, and social sensing, may help to explain probability and impacts of urban flooding. Several spatial datasets have been recently made available in the Netherlands, including rainfall-related incident reports made by citizens, spatially distributed rain depths, semidistributed socioeconomic information, and buildings age. Inspecting the potential of this data to explain the occurrence of rainfall related incidents has not been done yet. Multivariate analysis tools for describing communities and environmental patterns have been previously developed and used in the field of study of ecology. The objective of this paper is to outline opportunities for these tools to explore urban flooding risks patterns in the mentioned datasets. To that end, a cluster analysis is performed. Results indicate that incidence of rainfall-related impacts is higher in areas characterized by older infrastructure and higher population density.
Full Text Available Risk analysis has become a top priority for authorities and stakeholders in many European countries, with the aim of reducing flooding risk, considering the population's needs and improving risk awareness. Within this context, two methodological pieces have been developed in the period 2009–2011 within the SUFRI project (Sustainable Strategies of Urban Flood Risk Management with non-structural measures to cope with the residual risk, 2nd ERA-Net CRUE Funding Initiative. First, the "SUFRI Methodology for pluvial and river flooding risk assessment in urban areas to inform decision-making" provides a comprehensive and quantitative tool for flood risk analysis. Second, the "Methodology for investigation of risk awareness of the population concerned" presents the basis to estimate current risk from a social perspective and identify tendencies in the way floods are understood by citizens. Outcomes of both methods are integrated in this paper with the aim of informing decision making on non-structural protection measures. The results of two case studies are shown to illustrate practical applications of this developed approach. The main advantage of applying the methodology herein presented consists in providing a quantitative estimation of flooding risk before and after investing in non-structural risk mitigation measures. It can be of great interest for decision makers as it provides rational and solid information.
Nguyen, Hong Quan; Huynh, Thi Thao Nguyen; Pathirana, Assela; Van der Steen, Peter
Public health risks from urban flooding are a global concern. Contaminated floodwater may expose residents living in cities as they are in direct contact with the water. However, the recent literature does not provide much information about this issue, especially for developing countries. In this paper, the health risk due to a flood event occurred in Can Tho City (Mekong Delta, Vietnam) on 7 October 2013 was investigated. The Quantitative Microbial Risk Assessment method was used in this study. The data showed that the pathogen concentrations were highly variable during the flood event and exceeded water standards for surface water. Per 10,000 people in contact with the floodwater, we found Salmonella caused the highest number of infections to adults and children (137 and 374, respectively), while E. coli caused 4 and 12 cases, per single event, respectively. The results show that further investigations on health risk related to flood issues in Can Tho City are required, especially because of climate change and urbanization. In addition, activities to raise awareness- about floods, e.g., "living with floods", in the Mekong Delta should also consider health risk issues.
Escuder-Bueno, I.; Castillo-Rodríguez, J. T.; Zechner, S.; Jöbstl, C.; Perales-Momparler, S.; Petaccia, G.
Risk analysis has become a top priority for authorities and stakeholders in many European countries, with the aim of reducing flooding risk, considering the population's needs and improving risk awareness. Within this context, two methodological pieces have been developed in the period 2009-2011 within the SUFRI project (Sustainable Strategies of Urban Flood Risk Management with non-structural measures to cope with the residual risk, 2nd ERA-Net CRUE Funding Initiative). First, the "SUFRI Methodology for pluvial and river flooding risk assessment in urban areas to inform decision-making" provides a comprehensive and quantitative tool for flood risk analysis. Second, the "Methodology for investigation of risk awareness of the population concerned" presents the basis to estimate current risk from a social perspective and identify tendencies in the way floods are understood by citizens. Outcomes of both methods are integrated in this paper with the aim of informing decision making on non-structural protection measures. The results of two case studies are shown to illustrate practical applications of this developed approach. The main advantage of applying the methodology herein presented consists in providing a quantitative estimation of flooding risk before and after investing in non-structural risk mitigation measures. It can be of great interest for decision makers as it provides rational and solid information.
Dressler, Gunnar; Mueller, Birgit; Frank, Karin; Kuhlicke, Christian
Natural disasters and in particular floods have become a strong threat to urban communities in the last decades. In just eleven years (2002, 2013) two centenary river floods have hit Eastern Germany, causing damages of 9.1 billion € (2002) and 6.7 billion € (2013, first estimate), making them the most costly flood events in German history. Many cities in the Free State of Saxony that were strongly hit by both floods are additionally challenged by demographic change with an ageing society and outmigration leading to population shrinkage. This also constrains the coping capacity of disaster management services, especially those of volunteer-based disaster management organisations such as fire brigades, leading to an increased vulnerability of the community at risk. On the other hand, new technologies such as social media have led to rapid information spread and self-organisation of tremendous numbers of civil volunteers willing to help. How do responsible organisations deal with the challenges associated with demographic change, as well as with expected increases in flood frequency and intensity, and what strategies could enhance their performance in the future? To explore these questions, we developed an agent-based simulation model. It is based on socio-demographic settings of the community, communication and coordination structures of disaster management as well as transportation infrastructure for resources and emergency forces. The model is developed in exchange with relevant stakeholders including experts of local disaster management organisations and authority representatives. The goal of the model is to a) assess the performance of disaster management organisations and determine performance limits with respect to forecast lead times and respective coping times of disaster management organisations and b) use it as a discussion tool with these organisations and authorities to identify weak points as well as new options and strategies to ensure protection
Norman, L.M.; Feller, M.; Phillip, Guertin D.
The sister-city area of Nogales, Arizona, and Nogales, Sonora, Mexico, is known collectively as Ambos (both) Nogales. This area was historically one city and was administratively divided by the Gadsden Purchase in 1853. These arid-lands have limited and sensitive natural resources. Environmental planning can support sustainable development to accommodate the predicted influx of population. The objective of this research is to quantify the amount of predicted urban growth for the Ambos Nogales watershed to support future planning for sustainable development. Two modeling regimes are explored. Our goal is to identify possible growth patterns associated with the twin-city area as a whole and with the two cities modeled as separate entities. We analyzed the cross-border watershed using regression analysis from satellite images from 1975, 1983, 1996, and 2002 and created urban area classifications. We used these classifications as input to the urban growth model, SLEUTH, to simulate likely patterns of development and define projected conversion probabilities. Model results indicate that the two cities are undergoing very different patterns of change and identify locations of expected growth based on historical development. Growth in Nogales, Arizona is stagnant while the urban area in Nogales, Sonora is exploding. This paper demonstrates an application that portrays how future binational urban growth could develop and affect the environment. This research also provides locations of potential growth for use in city planning.
Löwe, Roland; Urich, Christian; Sto. Domingo, Nina; Mark, Ole; Deletic, Ana; Arnbjerg-Nielsen, Karsten
While models can quantify flood risk in great detail, the results are subject to a number of deep uncertainties. Climate dependent drivers such as sea level and rainfall intensities, population growth and economic development all have a strong influence on future flood risk, but future developments can only be estimated coarsely. In such a situation, robust decision making frameworks call for the systematic evaluation of mitigation measures against ensembles of potential futures. We have coupled the urban development software DAnCE4Water and the 1D-2D hydraulic simulation package MIKE FLOOD to create a framework that allows for such systematic evaluations, considering mitigation measures under a variety of climate futures and urban development scenarios. A wide spectrum of mitigation measures can be considered in this setup, ranging from structural measures such as modifications of the sewer network over local retention of rainwater and the modification of surface flow paths to policy measures such as restrictions on urban development in flood prone areas or master plans that encourage compact development. The setup was tested in a 300 ha residential catchment in Melbourne, Australia. The results clearly demonstrate the importance of considering a range of potential futures in the planning process. For example, local rainwater retention measures strongly reduce flood risk a scenario with moderate increase of rain intensities and moderate urban growth, but their performance strongly varies, yielding very little improvement in situations with pronounced climate change. The systematic testing of adaptation measures further allows for the identification of so-called adaptation tipping points, i.e. levels for the drivers of flood risk where the desired level of flood risk is exceeded despite the implementation of (a combination of) mitigation measures. Assuming a range of development rates for the drivers of flood risk, such tipping points can be translated into
Keum, H.; Han, K.; Kim, H.; Ha, C.
The risk of urban flooding has been increasing due to heavy rainfall, flash flooding and rapid urbanization. Rainwater pumping stations, underground reservoirs are used to actively take measures against flooding, however, flood damage from lowlands continues to occur. Inundation in urban areas has resulted in overflow of sewer. Therefore, it is important to implement a network system that is intricately entangled within a city, similar to the actual physical situation and accurate terrain due to the effects on buildings and roads for accurate two-dimensional flood analysis. The purpose of this study is to propose an optimal scenario construction procedure watershed partitioning and parameterization for urban runoff analysis and pipe network analysis, and to increase the accuracy of flooded area prediction through coupled model. The establishment of optimal scenario procedure was verified by applying it to actual drainage in Seoul. In this study, optimization was performed by using four parameters such as Manning's roughness coefficient for conduits, watershed width, Manning's roughness coefficient for impervious area, Manning's roughness coefficient for pervious area. The calibration range of the parameters was determined using the SWMM manual and the ranges used in the previous studies, and the parameters were estimated using the automatic calibration method PEST. The correlation coefficient showed a high correlation coefficient for the scenarios using PEST. The RPE and RMSE also showed high accuracy for the scenarios using PEST. In the case of RPE, error was in the range of 13.9-28.9% in the no-parameter estimation scenarios, but in the scenario using the PEST, the error range was reduced to 6.8-25.7%. Based on the results of this study, it can be concluded that more accurate flood analysis is possible when the optimum scenario is selected by determining the appropriate reference conduit for future urban flooding analysis and if the results is applied to various
Soz, Salman Anees; Kryspin-Watson, Jolanta; Stanton-Geddes, Zuzana
This Knowledge Note explores the role of green infrastructure solutions in urban flood risk management. Green infrastructure solutions represent an approach that focuses on using natural processes for managing wet weather impacts while delivering environmental, social, and economic benefits. Green infrastructure solutions, such as wetlands, bioshields, buffer zones, green roofing, street s...
Simm, J.; Jordan, D.; Topple, A.; Mokhov, I.; Pyayt, A.; Abdoun, T.; Bennett, V.; Broekhuijsen, J.; Meijer, R.; Klijn, F.; Schweckendiek, T.
The UrbanFlood project is creating an Early Warning System framework that can be used to link sensors via the Internet to predictive models and emergency warning systems. The project includes four pilot sites to apply and validate at full scale the technology being developed in the project:
Floods are common in the United States. Weather such as heavy rain, thunderstorms, hurricanes, or tsunamis can ... is breached, or when a dam breaks. Flash floods, which can develop quickly, often have a dangerous ...
Kim, Moon H.; Morlock, Scott E.; Arihood, Leslie D.; Kiesler, James L.
Near-real-time and forecast flood-inundation mapping products resulted from a pilot study for an 11-mile reach of the White River in Indianapolis. The study was done by the U.S. Geological Survey (USGS), Indiana Silver Jackets hazard mitigation taskforce members, the National Weather Service (NWS), the Polis Center, and Indiana University, in cooperation with the City of Indianapolis, the Indianapolis Museum of Art, the Indiana Department of Homeland Security, and the Indiana Department of Natural Resources, Division of Water. The pilot project showed that it is technically feasible to create a flood-inundation map library by means of a two-dimensional hydraulic model, use a map from the library to quickly complete a moderately detailed local flood-loss estimate, and automatically run the hydraulic model during a flood event to provide the maps and flood-damage information through a Web graphical user interface. A library of static digital flood-inundation maps was created by means of a calibrated two-dimensional hydraulic model. Estimated water-surface elevations were developed for a range of river stages referenced to a USGS streamgage and NWS flood forecast point colocated within the study reach. These maps were made available through the Internet in several formats, including geographic information system, Keyhole Markup Language, and Portable Document Format. A flood-loss estimate was completed for part of the study reach by using one of the flood-inundation maps from the static library. The Federal Emergency Management Agency natural disaster-loss estimation program HAZUS-MH, in conjunction with local building information, was used to complete a level 2 analysis of flood-loss estimation. A Service-Oriented Architecture-based dynamic flood-inundation application was developed and was designed to start automatically during a flood, obtain near real-time and forecast data (from the colocated USGS streamgage and NWS flood forecast point within the study reach
Full Text Available In recent years, the Taiwan government has established a number of flood control facilities such as dikes, pumping stations and drainage systems to effectively reduce downstream flooding. However, with continued development and urbanization of catchment areas, the original designs of most flood control facilities have become outdated. Hillside lands in the upper and middle reaches of river basins have undergone urban development through unsound engineering practices, paving the way for heavy downstream flooding. Therefore, proper river basin management should include both upstream and downstream sides. The main purpose of the paper is to simulate non-urban inundation areas with various degrees of development (0%, 10%, 20%, 40% and 60%, over two different return periods of 25 years and 200 years, for intensive rainfall events in the Shi-Chi District, Taiwan. Through hydrological analysis and numerical simulations of inundation, quantitative data on inundation potential have been established based on the land development conditions along the hillsides on the upper and middle reaches of the Keelung River Basin. The simulated results show that the increase in the extent of land development in the upper reaches causes an increase in the area and depth of inundation, resulting in an increased risk of flooding in downstream areas. If the land-use policy makers in the upper reaches of the river basin’s hillsides do not properly manage the land development, the risk of flooding in downstream areas will increase. In such an event, the policy makers should first review the situation to understand the problem with the consideration of this study. Thus, proper development and flood mitigation in hillsides can be established.
Villarini, G.; Smith, J.A.; Serinaldi, F.; Bales, J.; Bates, P.D.; Krajewski, W.F.
Flood frequency analysis in urban watersheds is complicated by nonstationarities of annual peak records associated with land use change and evolving urban stormwater infrastructure. In this study, a framework for flood frequency analysis is developed based on the Generalized Additive Models for Location, Scale and Shape parameters (GAMLSS), a tool for modeling time series under nonstationary conditions. GAMLSS is applied to annual maximum peak discharge records for Little Sugar Creek, a highly urbanized watershed which drains the urban core of Charlotte, North Carolina. It is shown that GAMLSS is able to describe the variability in the mean and variance of the annual maximum peak discharge by modeling the parameters of the selected parametric distribution as a smooth function of time via cubic splines. Flood frequency analyses for Little Sugar Creek (at a drainage area of 110 km2) show that the maximum flow with a 0.01-annual probability (corresponding to 100-year flood peak under stationary conditions) over the 83-year record has ranged from a minimum unit discharge of 2.1 m3 s- 1 km- 2 to a maximum of 5.1 m3 s- 1 km- 2. An alternative characterization can be made by examining the estimated return interval of the peak discharge that would have an annual exceedance probability of 0.01 under the assumption of stationarity (3.2 m3 s- 1 km- 2). Under nonstationary conditions, alternative definitions of return period should be adapted. Under the GAMLSS model, the return interval of an annual peak discharge of 3.2 m3 s- 1 km- 2 ranges from a maximum value of more than 5000 years in 1957 to a minimum value of almost 8 years for the present time (2007). The GAMLSS framework is also used to examine the links between population trends and flood frequency, as well as trends in annual maximum rainfall. These analyses are used to examine evolving flood frequency over future decades. ?? 2009 Elsevier Ltd.
Villarini, Gabriele; Smith, James A.; Serinaldi, Francesco; Bales, Jerad; Bates, Paul D.; Krajewski, Witold F.
Flood frequency analysis in urban watersheds is complicated by nonstationarities of annual peak records associated with land use change and evolving urban stormwater infrastructure. In this study, a framework for flood frequency analysis is developed based on the Generalized Additive Models for Location, Scale and Shape parameters (GAMLSS), a tool for modeling time series under nonstationary conditions. GAMLSS is applied to annual maximum peak discharge records for Little Sugar Creek, a highly urbanized watershed which drains the urban core of Charlotte, North Carolina. It is shown that GAMLSS is able to describe the variability in the mean and variance of the annual maximum peak discharge by modeling the parameters of the selected parametric distribution as a smooth function of time via cubic splines. Flood frequency analyses for Little Sugar Creek (at a drainage area of 110km) show that the maximum flow with a 0.01-annual probability (corresponding to 100-year flood peak under stationary conditions) over the 83-year record has ranged from a minimum unit discharge of 2.1mskm to a maximum of 5.1mskm. An alternative characterization can be made by examining the estimated return interval of the peak discharge that would have an annual exceedance probability of 0.01 under the assumption of stationarity (3.2mskm). Under nonstationary conditions, alternative definitions of return period should be adapted. Under the GAMLSS model, the return interval of an annual peak discharge of 3.2mskm ranges from a maximum value of more than 5000 years in 1957 to a minimum value of almost 8 years for the present time (2007). The GAMLSS framework is also used to examine the links between population trends and flood frequency, as well as trends in annual maximum rainfall. These analyses are used to examine evolving flood frequency over future decades.
Full Text Available The behaviour of the urban network infrastructures, and their interactions during flood events, will have direct and indirect consequences on the flood risk level in the built environment. By urban network infrastructures we include all the urban technical networks like transportation, energy, water supply, waste water, telecommunication…able to spread the flood risk in cities, qualified as critical infrastructures due to their major roles for modern living standards. From history, most of cities in the world have been built close to coast lines or to river to beneficiate this means of communication and trade. Step by step, to avoid being flooded, defences like levees have been built. The capacity of the levees to retain the floods depends on their conditions, their performance level and the capacity of the authorities to well maintain these infrastructures. But recent history shows the limits of a flood risk management strategy focused on protection, leading to levee breaks these last decades. Then, in case of levee break, cities will be flooded. The urban technical networks, due to the way they have been designed, their conditions and their locations in the city, will play a major role in the diffusion of the flood extent. Also, the flood risk will have consequences in some not flooded neighbourhoods due to networks collapses and complex interdependencies. This article describes some methods to design spatial decision support systems in that context.
Full Text Available Flood simulation and forecasting in various types of watersheds is a hot issue in hydrology. Conceptual hydrological models have been widely applied to flood forecasting for decades. With the development of economy, modern China faces with severe flood disasters in all types of watersheds include humid, semi-humid semi-arid and arid watersheds. However, conceptual model-based flood forecasting in semi-humid semi-arid and arid regions is still challenging. To investigate the applicability of conceptual hydrological models for flood forecasting in the above mentioned regions, three typical conceptual models, include Xinanjiang (XAJ, mix runoff generation (MIX and northern Shannxi (NS, are applied to 3 humid, 3 semi-humid semi-arid, and 3 arid watersheds. The rainfall-runoff data of the 9 watersheds are analyzed based on statistical analysis and information theory, and the model performances are compared and analyzed based on boxplots and scatter plots. It is observed the complexity of drier watershed data is higher than that of the wetter watersheds. This indicates the flood forecasting is harder in drier watersheds. Simulation results indicate all models perform satisfactorily in humid watersheds and only NS model is applicable in arid watersheds. Model with consideration of saturation excess runoff generation (XAJ and MIX perform better than the infiltration excess-based NS model in semi-humid semi-arid watersheds. It is concluded more accurate mix runoff generation theory, more stable and efficient numerical solution of infiltration equation and rainfall data with higher spatial-temporal resolution are main obstacles for conceptual model-based flood simulation and forecasting.
Krajewski, W. F.; Della Libera Zanchetta, A.; Mantilla, R.; Demir, I.
This work explores the use of hydroinformatics tools to provide an user friendly and accessible interface for executing and assessing the output of realtime flood forecasts using distributed hydrological models. The main result is the implementation of a web system that uses an Iowa Flood Information System (IFIS)-based environment for graphical displays of rainfall-runoff simulation results for both real-time and past storm events. It communicates with ASYNCH ODE solver to perform large-scale distributed hydrological modeling based on segmentation of the terrain into hillslope-link hydrologic units. The cyber-platform also allows hindcast of model performance by testing multiple model configurations and assumptions of vertical flows in the soils. The scope of the currently implemented system is the entire set of contributing watersheds for the territory of the state of Iowa. The interface provides resources for visualization of animated maps for different water-related modeled states of the environment, including flood-waves propagation with classification of flood magnitude, runoff generation, surface soil moisture and total water column in the soil. Additional tools for comparing different model configurations and performing model evaluation by comparing to observed variables at monitored sites are also available. The user friendly interface has been published to the web under the URL http://ifis.iowafloodcenter.org/ifis/sc/modelplus/.
Comer, Joanne; Indiana Olbert, Agnieszka; Nash, Stephen; Hartnett, Michael
Urban developments in coastal zones are often exposed to natural hazards such as flooding. In this research, a state-of-the-art, multi-scale nested flood (MSN_Flood) model is applied to simulate complex coastal-fluvial urban flooding due to combined effects of tides, surges and river discharges. Cork city on Ireland's southwest coast is a study case. The flood modelling system comprises a cascade of four dynamically linked models that resolve the hydrodynamics of Cork Harbour and/or its sub-region at four scales: 90, 30, 6 and 2 m. Results demonstrate that the internalization of the nested boundary through the use of ghost cells combined with a tailored adaptive interpolation technique creates a highly dynamic moving boundary that permits flooding and drying of the nested boundary. This novel feature of MSN_Flood provides a high degree of choice regarding the location of the boundaries to the nested domain and therefore flexibility in model application. The nested MSN_Flood model through dynamic downscaling facilitates significant improvements in accuracy of model output without incurring the computational expense of high spatial resolution over the entire model domain. The urban flood model provides full characteristics of water levels and flow regimes necessary for flood hazard identification and flood risk assessment.
Tuyls, Damian Murla; Nielsen, Rasmus; Thorndahl, Søren Liedtke
A return period assessment of urban flood has been performed and its adhered impact of rainfall variability studied over a urban drainage catchment area in Aalborg, Denmark. Recorded rainfall from 7 rain gauges has been used, located in a range of 7.5Km and for a period varying form 18-37 years....... Return period of rainfall and flood at catchment and local scale has been estimated, its derived ambiguities analysed and the variability of rain gauge based rainfall investigated regarding to flood estimation results. Results show a clear contrast between rainfall and flood return period estimates...
Seto, K. C.; Guneralp, B.; Hutyra, L.
Urban land cover change threatens biodiversity and affects ecosystem productivity through loss of habitat, biomass, and carbon storage. Yet, despite projections that world urban populations will increase to 4.3 billion by 2030, little is known about future locations, magnitudes, and rates of urban expansion. Here we develop the first global probabilistic forecasts of urban land cover change and explore the impacts on biodiversity hotspots and tropical carbon biomass. If current trends in population density continue, then by 2030, urban land cover will expand between 800,000 and 3.3 million km2, representing a doubling to five-fold increase from the global urban land cover in 2000. This would result in considerable loss of habitats in key biodiversity hotspots, including the Guinean forests of West Africa, Tropical Andes, Western Ghats and Sri Lanka. Within the pan-tropics, loss in forest biomass from urban expansion is estimated to be 1.38 PgC (0.05 PgC yr-1), equal to approximately 5% of emissions from tropical land use change. Although urbanization is often considered a local issue, the aggregate global impacts of projected urban expansion will require significant policy changes to affect future growth trajectories to minimize global biodiversity and forest carbon losses.
Smidt, Samuel J; Tayyebi, Amin; Kendall, Anthony D; Pijanowski, Bryan C; Hyndman, David W
Urbanization onto adjacent farmlands directly reduces the agricultural area available to meet the resource needs of a growing society. Soil conservation is a common objective in urban planning, but little focus has been placed on targeting soil value as a metric for conservation. This study assigns commodity and water storage values to the agricultural soils across all of the watersheds in Michigan's Lower Peninsula to evaluate how cities might respond to a soil conservation-based urbanization strategy. Land Transformation Model (LTM) simulations representing both traditional and soil conservation-based urbanization, are used to forecast urban area growth from 2010 to 2050 at five year intervals. The expansion of urban areas onto adjacent farmland is then evaluated to quantify the conservation effects of soil-based development. Results indicate that a soil-based protection strategy significantly conserves total farmland, especially more fertile soils within each soil type. In terms of revenue, ∼$88 million (in current dollars) would be conserved in 2050 using soil-based constraints, with the projected savings from 2011 to 2050 totaling more than $1.5 billion. Soil-based urbanization also increased urban density for each major metropolitan area. For example, there were 94,640 more acres directly adjacent to urban land by 2050 under traditional development compared to the soil-based urbanization strategy, indicating that urban sprawl was more tightly contained when including soil value as a metric to guide development. This study indicates that implementing a soil-based urbanization strategy would better satisfy future agricultural resource needs than traditional urban planning. Copyright © 2018. Published by Elsevier Ltd.
Full Text Available Remote sensing can play a key role in risk assessment and management, especially when several concurrent factors coexist, such as a predisposition to natural disasters and the urban sprawl, spreading over highly vulnerable areas. In this context, multitemporal analysis can provide decision-makers with tools and information to reduce the impacts of disasters (e.g. flooding and to encourage a sustainable development. The present work focuses on the employment of multispectral satellite imagery to produce multitemporal land use/cover maps for the city of Dhaka, which is subject to frequent flooding events. In particular, the evaluation of the urban growth, the analysis of the annual dynamics of flooding and the study of the 2004 catastrophic event were performed. For the change-detection procedure, Landsat images were used. These images allow the quantification of the very rapid growth of the metropolis, with an increase in built-up areas from 75 to 111 km2. The image of 2009 showed that an ordinary flood affects about 115 km2 (on a studied area of 591 km2. On the other hand, the analysis of the 2004 extreme flooding event, performed on a wider area, showed that the affected lands added up to 750 km2 (on about 3845 km2.
Full Text Available Many floodplains are excluded from urban development because the floods cause considerable damage to people’s lives and properties. This requires the development of new approaches to flood management and mitigation for support sustainable urban development. In present study as the measures for mitigation of flash floods, the regulation of river flow by the system of detention reservoirs for flood diversion with dams, which do not need any operation management, are analyzed concerning of Far East region of Russia. The main objective of this paper is to develop a method for analysis how the dam site selection meets the environmental criterion. The method to justify a selection of self-regulated flood dam parameters, primarily a height of a dam and its location on a water stream, providing minimization of impact on the environment have been developed. The result for Selemdzha river basin in Far East monsoon region of Russian Federation is analyzed. The result shows the robustness of the method.
Hong Quan Nguyen
Full Text Available Public health risks from urban flooding are a global concern. Contaminated floodwater may expose residents living in cities as they are in direct contact with the water. However, the recent literature does not provide much information about this issue, especially for developing countries. In this paper, the health risk due to a flood event occurred in Can Tho City (Mekong Delta, Vietnam on 7 October 2013 was investigated. The Quantitative Microbial Risk Assessment method was used in this study. The data showed that the pathogen concentrations were highly variable during the flood event and exceeded water standards for surface water. Per 10,000 people in contact with the floodwater, we found Salmonella caused the highest number of infections to adults and children (137 and 374, respectively, while E. coli caused 4 and 12 cases, per single event, respectively. The results show that further investigations on health risk related to flood issues in Can Tho City are required, especially because of climate change and urbanization. In addition, activities to raise awareness- about floods, e.g., “living with floods”, in the Mekong Delta should also consider health risk issues.
Huynh, Thi Thao Nguyen; Van der Steen, Peter
Public health risks from urban flooding are a global concern. Contaminated floodwater may expose residents living in cities as they are in direct contact with the water. However, the recent literature does not provide much information about this issue, especially for developing countries. In this paper, the health risk due to a flood event occurred in Can Tho City (Mekong Delta, Vietnam) on 7 October 2013 was investigated. The Quantitative Microbial Risk Assessment method was used in this study. The data showed that the pathogen concentrations were highly variable during the flood event and exceeded water standards for surface water. Per 10,000 people in contact with the floodwater, we found Salmonella caused the highest number of infections to adults and children (137 and 374, respectively), while E. coli caused 4 and 12 cases, per single event, respectively. The results show that further investigations on health risk related to flood issues in Can Tho City are required, especially because of climate change and urbanization. In addition, activities to raise awareness- about floods, e.g., “living with floods”, in the Mekong Delta should also consider health risk issues. PMID:29189715
Laurent Guillaume Courty
Full Text Available The growth of urban population, combined with an increase of extreme events due to climate change call for a better understanding and representation of urban floods. The uncertainty in rainfall distribution is one of the most important factors that affects the watershed response to a given precipitation event. However, most of the investigations on this topic have considered theoretical scenarios, with little reference to case studies in the real world. This paper incorporates the use of spatially-variable precipitation data from a long-range radar in the simulation of the severe floods that impacted the city of Hull, U.K., in June 2007. This radar-based rainfall field is merged with rain gauge data using a Kriging with External Drift interpolation technique. The utility of this spatially-variable information is investigated through the comparison of computed flooded areas (uniform and radar against those registered by public authorities. Both results show similar skills at reproducing the real event, but differences in the total precipitated volumes, water depths and flooded areas are illustrated. It is envisaged that in urban areas and with the advent of higher resolution radars, these differences will be more important and call for further investigation.
Urbanization is the world development trend for the past century, and the developing countries have been experiencing much rapider urbanization in the past decades. Urbanization brings many benefits to human beings, but also causes negative impacts, such as increasing flood risk. Impact of urbanization on flood response has long been observed, but quantitatively studying this effect still faces great challenges. For example, setting up an appropriate hydrological model representing the changed flood responses and determining accurate model parameters are very difficult in the urbanized or urbanizing watershed. In the Pearl River Delta area, rapidest urbanization has been observed in China for the past decades, and dozens of highly urbanized watersheds have been appeared. In this study, a physically based distributed watershed hydrological model, the Liuxihe model is employed and revised to simulate the hydrological processes of the highly urbanized watershed flood in the Pearl River Delta area. A virtual soil type is then defined in the terrain properties dataset, and its runoff production and routing algorithms are added to the Liuxihe model. Based on a parameter sensitive analysis, the key hydrological processes of a highly urbanized watershed is proposed, that provides insight into the hydrological processes and for parameter optimization. Based on the above analysis, the model is set up in the Songmushan watershed where there is hydrological data observation. A model parameter optimization and updating strategy is proposed based on the remotely sensed LUC types, which optimizes model parameters with PSO algorithm and updates them based on the changed LUC types. The model parameters in Songmushan watershed are regionalized at the Pearl River Delta area watersheds based on the LUC types of the other watersheds. A dozen watersheds in the highly urbanized area of Dongguan City in the Pearl River Delta area were studied for the flood response changes due to
Anil Kumar Kar
New hydrological insights for the region: This study establishes different possible key RG networks using Hall’s method, analytical hierarchical process (AHP, self organization map (SOM and hierarchical clustering (HC using the characteristics of each rain gauge occupied Thiessen polygon area. Efficiency of the key networks is tested by artificial neural network (ANN, Fuzzy and NAM rainfall-runoff models. Furthermore, flood forecasting has been carried out using the three most effective RG networks which uses only 7 RGs instead of 14 gauges established in the Kantamal sub-catchment, Mahanadi basin. The Fuzzy logic applied on the key RG network derived using AHP has shown the best result for flood forecasting with efficiency of 82.74% for 1-day lead period. This study demonstrates the design procedure of key RG network for effective flood forecasting particularly when there is difficulty in gathering the information from all RGs.
Full Text Available This work presents the application of the multi-temporal approach of the Model Conditional Processor (MCP-MT for predictive uncertainty (PU estimation in the Godavari River basin, India. MCP-MT is developed for making probabilistic Bayesian decision. It is the most appropriate approach if the uncertainty of future outcomes is to be considered. It yields the best predictive density of future events and allows determining the probability that a critical warning threshold may be exceeded within a given forecast time. In Bayesian decision-making, the predictive density represents the best available knowledge on a future event to address a rational decision-making process. MCP-MT has already been tested for case studies selected in Italian river basins, showing evidence of improvement of the effectiveness of operative real-time flood forecasting systems. The application of MCP-MT for two river reaches selected in the Godavari River basin, India, is here presented and discussed by considering the stage forecasts provided by a deterministic model, STAFOM-RCM, and hourly dataset based on seven monsoon seasons in the period 2001–2010. The results show that the PU estimate is useful for finding the exceedance probability for a given hydrometric threshold as function of the forecast time up to 24 h, demonstrating the potential usefulness for supporting real-time decision-making. Moreover, the expected value provided by MCP-MT yields better results than the deterministic model predictions, with higher Nash–Sutcliffe coefficients and lower error on stage forecasts, both in term of mean error and standard deviation and root mean square error.
Zhang, J.; Fang, N. Z.
A potential flood forecast system is under development for the Upper Trinity River Basin (UTRB) in North Central of Texas using the WRF-Hydro model. The Routing Application for the Parallel Computation of Discharge (RAPID) is utilized as channel routing module to simulate streamflow. Model performance analysis was conducted based on three quantitative precipitation estimates (QPE): the North Land Data Assimilation System (NLDAS) rainfall, the Multi-Radar Multi-Sensor (MRMS) QPE and the National Centers for Environmental Prediction (NCEP) quality-controlled stage IV estimates. Prior to hydrologic simulation, QPE performance is assessed on two time scales (daily and hourly) using the Community Collaborative Rain, Hail and Snow Network (CoCoRaHS) and Hydrometeorological Automated Data System (HADS) hourly products. The calibrated WRF-Hydro model was then evaluated by comparing the simulated against the USGS observed using various QPE products. The results imply that the NCEP stage IV estimates have the best accuracy among the three QPEs on both time scales, while the NLDAS rainfall performs poorly because of its coarse spatial resolution. Furthermore, precipitation bias demonstrates pronounced impact on flood forecasting skills, as the root mean squared errors are significantly reduced by replacing NLDAS rainfall with NCEP stage IV estimates. This study also demonstrates that accurate simulated results can be achieved when initial soil moisture values are well understood in the WRF-Hydro model. Future research effort will therefore be invested on incorporating data assimilation with focus on initial states of the soil properties for UTRB.
Full Text Available The effectiveness of alert systems for civil protection purposes, defined as the ability to minimize the level of risk in a region subjected to an imminent flood event, strongly depends on availability and exploitability of information. It also depends on technical expertise and the ability to easily manage the civil protection actions through the organization into standardized procedures. Hydro-geologic and hydraulic risk estimation, based on the combination of different technical issues (in this case meteorological, hydro-geological, hydraulic matters, but also socio-economic ones, requires the integration between quasi-static and time-varying information within the same operative platform. Beside the real-time data exchange, a Decision Support System must provide tools which enable knowledge sharing among the civil protection centres. Moreover, due to the amount and heterogeneity of information, quality procedures become necessary to handle all forecasting and monitoring routines within operative centres, according to the latest national directive. In Italy procedures on the civil protection matter have been condensed into the Prime Minister's Directive (27 February 2004. STORM3, an innovative management and monitoring System for real-time flood forecasting and warning, takes in the Directive, supporting the operator step by step within the different phases of civil protection activities.
Löwe, Roland; Mikkelsen, Peter Steen; Madsen, Henrik
We present stochastic flow forecasts to be used in a real-time control setup for urban drainage systems. The forecasts are generated using greybox models with rain gauge and radar rainfall observations as input. Predictions are evaluated as intervals rather than just mean values. We obtain...
Zhu, Guangyu; Wang, Li; Zhang, Peng; Song, Kang
Urban road travel time is an important parameter to reflect the traffic flow state. Besides, it is one of the important parameters for the traffic management department to formulate guidance measures, provide traffic information service, and improve the efficiency of the detectors group. Therefore, it is crucial to improve the forecast accuracy of travel time in traffic management practice. Based on the analysis of the change-point and the ARIMA model, this paper constructs a model for the ma...
D. V. Kapsky
The paper provides investigation results pertaining to development of methodology for forecasting traffic accidents using a “conflict zone” method that considers potential danger for two typical urban areas, namely: signaled crossings and bumps that are made in the areas of zebra crossings and it also considers various types and kinds of conflicts. The investigations have made it possible to obtain various indices of threshold sensitivity in respect of potential risks and in relation to tra...
Babovic, F.; Mijic, A.; Madani, K.
Around the globe, urban areas are growing in both size and importance. However, due to the prevalence of impermeable surfaces within the urban fabric of cities these areas have a high risk of pluvial flooding. Due to the convergence of population growth and climate change the risk of pluvial flooding is growing. When designing solutions and adaptations to pluvial flood risk urban planners and engineers encounter a great deal of uncertainty due to model uncertainty, uncertainty within the data utilised, and uncertainty related to future climate and land use conditions. The interaction of these uncertainties leads to conditions of deep uncertainty. However, infrastructure systems must be designed and built in the face of this deep uncertainty. An Adaptation Tipping Points (ATP) methodology was used to develop a strategy to adapt an urban drainage system in the North East of London under conditions of deep uncertainty. The ATP approach was used to assess the current drainage system and potential drainage system adaptations. These adaptations were assessed against potential changes in rainfall depth and peakedness-defined as the ratio of mean to peak rainfall. These solutions encompassed both traditional and blue-green solutions that the Local Authority are known to be considering. This resulted in a set of Adaptation Pathways. However, theses pathways do not convey any information regarding the relative merits and demerits of the potential adaptation options presented. To address this a cost-benefit metric was developed that would reflect the solutions' costs and benefits under uncertainty. The resulting metric combines elements of the Benefits of SuDS Tool (BeST) with real options analysis in order to reflect the potential value of ecosystem services delivered by blue-green solutions under uncertainty. Lastly, it is discussed how a local body can utilise the adaptation pathways; their relative costs and benefits; and a system of local data collection to help guide
The challenges of monitoring and forecasting flash-flood-producing storm events in data-sparse and arid regions are explored using the Weather Research and Forecasting (WRF) Model (version 3.5) in conjunction with a range of available satellite, in situ, and reanalysis data. Here, we focus on characterizing the initial synoptic features and examining the impact of model parameterization and resolution on the reproduction of a number of flood-producing rainfall events that occurred over the western Saudi Arabian city of Jeddah. Analysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) data suggests that mesoscale convective systems associated with strong moisture convergence ahead of a trough were the major initial features for the occurrence of these intense rain events. The WRF Model was able to simulate the heavy rainfall, with driving convective processes well characterized by a high-resolution cloud-resolving model. The use of higher (1 km vs 5 km) resolution along the Jeddah coastline favors the simulation of local convective systems and adds value to the simulation of heavy rainfall, especially for deep-convection-related extreme values. At the 5-km resolution, corresponding to an intermediate study domain, simulation without a cumulus scheme led to the formation of deeper convective systems and enhanced rainfall around Jeddah, illustrating the need for careful model scheme selection in this transition resolution. In analysis of multiple nested WRF simulations (25, 5, and 1 km), localized volume and intensity of heavy rainfall together with the duration of rainstorms within the Jeddah catchment area were captured reasonably well, although there was evidence of some displacements of rainstorm events.
Andersen, Signe Tanja
D thesis is to identify the limitations and possibilities for optimising microbial risk assessments of urban flooding through more evidence-based solutions, including quantitative microbial data and hydrodynamic water quality models. The focus falls especially on the problem of data needs and the causes......, but also when wading through a flooded area. The results in this thesis have brought microbial risk assessments one step closer to more uniform and repeatable risk analysis by using actual and relevant measured data and hydrodynamic water quality models to estimate the risk from flooding caused...... are expected to increase in the future. To ensure public health during extreme rainfall, solutions are needed, but limited knowledge on microbial water quality, and related health risks, makes it difficult to implement microbial risk analysis as a part of the basis for decision making. The main aim of this Ph...
This is a multilayer feed forward neural network with a back- ... 2 with the input layer, one hidden layer, andthe output layer with only one node . Theactivation ..... Thepaper is aresult of theresearch project entitled FLOod Control. Decision ...
Pedersen, A. N.; Mikkelsen, Peter Steen; Arnbjerg-Nielsen, Karsten
hazards, rainfall and sea surge, are both important. The core in the methodology is the application of copula functions as an extension of one-dimensional risk analysis and projections of future climatic changes. The results for Greater Copenhagen indicate that the dependence between the hazards is weak......In coastal regions, several hazards may lead to floods, and if they occur concurrently, the damage will be higher than for the hazards individually. The paper outlines an approach for carrying out a risk analysis with several hazards and applies it on a case study in Greater Copenhagen where two...... and that climate change most likely will not increase the correlation. The overall change in flood return periods over a forecast horizon of 110 years are estimated to decrease by one to three orders of magnitude....
Full Text Available The adoption of 2007/60/EC Directive requires European countries to implement flood hazard and flood risk maps by the end of 2013. Flood risk is the product of flood hazard, vulnerability and exposure, all three to be estimated with comparable level of accuracy. The route to flood risk assessment is consequently much more than hydraulic modelling of inundation, that is hazard mapping. While hazard maps have already been implemented in many countries, quantitative damage and risk maps are still at a preliminary level. A parsimonious quasi-2-D hydraulic model is here adopted, having many advantages in terms of easy set-up. It is here evaluated as being accurate in flood depth estimation in urban areas with a high-resolution and up-to-date Digital Surface Model (DSM. The accuracy, estimated by comparison with marble-plate records of a historic flood in the city of Florence, is characterized in the downtown's most flooded area by a bias of a very few centimetres and a determination coefficient of 0.73. The average risk is found to be about 14 € m−2 yr−1, corresponding to about 8.3% of residents' income. The spatial distribution of estimated risk highlights a complex interaction between the flood pattern and the building characteristics. As a final example application, the estimated risk values have been used to compare different retrofitting measures. Proceeding through the risk estimation steps, a new micro-scale potential damage assessment method is proposed. This is based on the georeferenced census system as the optimal compromise between spatial detail and open availability of socio-economic data. The results of flood risk assessment at the census section scale resolve most of the risk spatial variability, and they can be easily aggregated to whatever upper scale is needed given that they are geographically defined as contiguous polygons. Damage is calculated through stage–damage curves, starting from census data on building type and
Denniss, A.; Tewkesbury, A.
Spaceborne remote sensors have been allowing us to build up a profile of planet earth for many years. With each new satellite launched we see the capabilities improve: new bands of data, higher resolution imagery, the ability to derive better elevation information. The combination of this geospatial data to create land cover and usage maps, all help inform catastrophe modelling systems. From Landsat 30m resolution to 2.44m QuickBird multispectral imagery; from 1m radar data collected by TerraSAR-X which enables rapid tracking of the rise and fall of a flood event, and will shortly have a twin satellite launched enabling elevation data creation; we are spoilt for choice in available data. However, just what is cost effective? It is always a question of choosing the appropriate level of input data detail for modelling, depending on the value of the risk. In the summer of 2007, the cost of the flooding in the UK was approximately £3bn and affected over 58,000 homes and businesses. When it comes to flood risk, we have traditionally considered rising river levels and surge tides, but with climate change and variations in our own construction behaviour, there are other factors to be taken into account. During those summer 2007 events, the Environment Agency suggested that around 70% of the properties damaged were the result of pluvial flooding, where high localised rainfall events overload localised drainage infrastructure, causing widespread flooding of properties and infrastructure. To create a risk model that is able to simulate such an event requires much more accurate source data than can be provided from satellite or radar. As these flood events cause considerable damage within relatively small, complex urban environments, therefore new high resolution remote sensing techniques have to be applied to better model these events. Detailed terrain data of England and Wales, plus cities in Scotland, have been produced by combining terrain measurements from the latest
Lehmann, T; Holzmann, H
The knowledge of the expected dimension of the flood peak is of major importance for security and warning services to take preventive measures. In this paper the authors want to introduce the concept of the Antecedent Precipitation Index (API) as a possible variable to estimate runoff warning classes. The aim was (a) to define API warning classes which correspond to runoff warning classes at a certain runoff gauge and (b) apply the method to ungauged basins. To consider time and state dependant rainfall losses a spatially distributed linear storage concept was applied to intercept the actual rainfall. The 3-parameter API function was fitted to several flood events at observed gauges within the district of lower Austria and lead to a set of optimized parameters. Through extreme value statistics the 1, 5 and 30 years API extremes were derived and set into correlation to the corresponding flood events. These API extremes together with the optimized API parameters were spatially interpolated and thus transferred to ungauged basins. The calculated flood events had the tendency to underestimate the smaller flood frequencies whereas the extreme flood classes could be reliably performed.
Ghazavi, Reza; Moafi Rabori, Ali; Ahadnejad Reveshty, Mohsen
Estimate design storm based on rainfall intensity–duration–frequency (IDF) curves is an important parameter for hydrologic planning of urban areas. The main aim of this study was to estimate rainfall intensities of Zanjan city watershed based on overall relationship of rainfall IDF curves and appropriate model of hourly rainfall estimation (Sherman method, Ghahreman and Abkhezr method). Hydrologic and hydraulic impacts of rainfall IDF curves change in flood properties was evaluated via Stormw...
Anders Skovgård Olsen
Full Text Available Estimating the expected annual damage (EAD due to flooding in an urban area is of great interest for urban water managers and other stakeholders. It is a strong indicator for a given area showing how vulnerable it is to flood risk and how much can be gained by implementing e.g., climate change adaptation measures. This study identifies and compares three different methods for estimating the EAD based on unit costs of flooding of urban assets. One of these methods was used in previous studies and calculates the EAD based on a few extreme events by assuming a log-linear relationship between cost of an event and the corresponding return period. This method is compared to methods that are either more complicated or require more calculations. The choice of method by which the EAD is calculated appears to be of minor importance. At all three case study areas it seems more important that there is a shift in the damage costs as a function of the return period. The shift occurs approximately at the 10 year return period and can perhaps be related to the design criteria for sewer systems. Further, it was tested if the EAD estimation could be simplified by assuming a single unit cost per flooded area. The results indicate that within each catchment this may be a feasible approach. However the unit costs varies substantially between different case study areas. Hence it is not feasible to develop unit costs that can be used to calculate EAD, most likely because the urban landscape is too heterogeneous.
Caparros-Midwood Daniel; Dawson Richard; Barr Stuart
A spatial optimization framework has been developed to help urban areas mitigate climate risks such as flooding and to curb resource use and greenhouse gas emissions. Measures required to address these issues often conflict with each other, for example more compact cities typically use less energy for transportation but increase runoff from high intensity rainfall events. Balancing potential trade-offs and maximizing synergies between these risks and vulnerabilities is therefore a multi-dimen...
Rianna, G.; Mercogliano, P.
Urbanization increases the flood risk because of heightened vulnerability, stemming from population concentration and hazard due to soil sealing affecting the largest part of urban settlements and reducing the concentration time of interested basins. Furthermore, current and future hazards are exacerbated by expected increases in extreme rainfall events due to Climate Changes (CC) making inadequate urban drainage infrastructures designed under the assumption of steady conditions. In this work, we present a modeling chain/algorithm to assess potential increase in pluvial flood hazard able to take into account CC forcing. The adopted simulation chain reckon on three main elements: Regional Climate Model, COSMO_CLM, dynamically downscaling GCM CMCC_CM (Scoccimarro et al., 2011) and optimized, at high resolution (about 8km), by Bucchignani et al. (2015) on Italy provide projections about precipitation up to 2100 under two concentration scenarios (RCP4.5 and RCP8.5). Such projections are used in Equidistance Quantile Mapping (EQM) approach, developed by Srivastav et al. (2014) to estimate expected variations in IDF (Intensity-Duration-Frequency) curves calculated through Generalized Extreme Value (GEV) approach on the basis of available rainfall data. To this aim, 1971-2000 observations are used as reference. Finally, a 1-D/2-D coupled urban drainage/flooding model forced by IDF (current and projected) is used to simulate storm-sewer surcharge and surface inundation to establish the variations in urban flooding risk. As test case is considered the city center of Naples (Southern Italy). In this respective, the sewage and urban drainage network is highly complex due to the historical and subsequent transformations of the city. Under such constraints, the reliability of the results maybe deeply conditioned by uncertainties not undermining the illustrative purposes of the work. Briefly, EQM returns a remarkable increase in extreme precipitations; such increase is driven by
A.C. Guy; T.M. DeSutter; F.X.M. Casey; R. Kolka; H. Hakk
Spring flooding of the Red River of the North (RR) is common, but little information exits on how these flood events affect water and overbank sediment quality within an urban area. With the threat of the spring 2009 flood in the RR predicted to be the largest in recorded history and the concerns about the flooding of farmsteads, outbuildings, garages, and basements,...
Arnbjerg-Nielsen, Karsten; Madsen, Henrik
with better decision support tools. Some of the developments are risk frameworks that encompass economic and/or ethic evaluation of climate change adaptation options and improved risk management. This line of development is based on a societal-based evaluation of maximizing the outcome for society...... in extreme precipitation has been observed, corresponding to an increase of design levels of at least 30 %. Analysis of climate change model output has given clear evidence, that further increases in extreme precipitation must be expected in the future due to anthropogenic emissions of greenhouse gasses...... and planned urban drainage solutions are shared between very different stakeholders and that current practices are leading to personal bankruptcy by those bearing the highest costs. Therefore solutions must be developed that are understandable and can be communicated between different stakeholders...
Kasiviswanathan, K.; Sudheer, K.
Artificial neural network (ANN) based hydrologic models have gained lot of attention among water resources engineers and scientists, owing to their potential for accurate prediction of flood flows as compared to conceptual or physics based hydrologic models. The ANN approximates the non-linear functional relationship between the complex hydrologic variables in arriving at the river flow forecast values. Despite a large number of applications, there is still some criticism that ANN's point prediction lacks in reliability since the uncertainty of predictions are not quantified, and it limits its use in practical applications. A major concern in application of traditional uncertainty analysis techniques on neural network framework is its parallel computing architecture with large degrees of freedom, which makes the uncertainty assessment a challenging task. Very limited studies have considered assessment of predictive uncertainty of ANN based hydrologic models. In this study, a novel method is proposed that help construct the prediction interval of ANN flood forecasting model during calibration itself. The method is designed to have two stages of optimization during calibration: at stage 1, the ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector, and during stage 2, the optimal variability of ANN parameters (obtained in stage 1) is identified so as to create an ensemble of predictions. During the 2nd stage, the optimization is performed with multiple objectives, (i) minimum residual variance for the ensemble mean, (ii) maximum measured data points to fall within the estimated prediction interval and (iii) minimum width of prediction interval. The method is illustrated using a real world case study of an Indian basin. The method was able to produce an ensemble that has an average prediction interval width of 23.03 m3/s, with 97.17% of the total validation data points (measured) lying within the interval. The derived
A. E. Sikorska
Full Text Available Urbanization and the resulting land-use change strongly affect the water cycle and runoff-processes in watersheds. Unfortunately, small urban watersheds, which are most affected by urban sprawl, are mostly ungauged. This makes it intrinsically difficult to assess the consequences of urbanization. Most of all, it is unclear how to reliably assess the predictive uncertainty given the structural deficits of the applied models. In this study, we therefore investigate the uncertainty of flood predictions in ungauged urban basins from structurally uncertain rainfall-runoff models. To this end, we suggest a procedure to explicitly account for input uncertainty and model structure deficits using Bayesian statistics with a continuous-time autoregressive error model. In addition, we propose a concise procedure to derive prior parameter distributions from base data and successfully apply the methodology to an urban catchment in Warsaw, Poland. Based on our results, we are able to demonstrate that the autoregressive error model greatly helps to meet the statistical assumptions and to compute reliable prediction intervals. In our study, we found that predicted peak flows were up to 7 times higher than observations. This was reduced to 5 times with Bayesian updating, using only few discharge measurements. In addition, our analysis suggests that imprecise rainfall information and model structure deficits contribute mostly to the total prediction uncertainty. In the future, flood predictions in ungauged basins will become more important due to ongoing urbanization as well as anthropogenic and climatic changes. Thus, providing reliable measures of uncertainty is crucial to support decision making.
P. Skougaard Kaspersen
Full Text Available The economic and human consequences of extreme precipitation and the related flooding of urban areas have increased rapidly over the past decades. Some of the key factors that affect the risks to urban areas include climate change, the densification of assets within cities and the general expansion of urban areas. In this paper, we examine and compare quantitatively the impact of climate change and recent urban development patterns on the exposure of four European cities to pluvial flooding. In particular, we investigate the degree to which pluvial floods of varying severity and in different geographical locations are influenced to the same extent by changes in urban land cover and climate change. We have selected the European cities of Odense, Vienna, Strasbourg and Nice for analyses to represent different climatic conditions, trends in urban development and topographical characteristics. We develop and apply a combined remote-sensing and flood-modelling approach to simulate the extent of pluvial flooding for a range of extreme precipitation events for historical (1984 and present-day (2014 urban land cover and for two climate-change scenarios (i.e. representative concentration pathways, RCP 4.5 and RCP 8.5. Changes in urban land cover are estimated using Landsat satellite imagery for the period 1984–2014. We combine the remote-sensing analyses with regionally downscaled estimates of precipitation extremes of current and expected future climate to enable 2-D overland flow simulations and flood-hazard assessments. The individual and combined impacts of urban development and climate change are quantified by examining the variations in flooding between the different simulations along with the corresponding uncertainties. In addition, two different assumptions are examined with regards to the development of the capacity of the urban drainage system in response to urban development and climate change. In the stationary approach, the capacity
Skougaard Kaspersen, Per; Høegh Ravn, Nanna; Arnbjerg-Nielsen, Karsten; Madsen, Henrik; Drews, Martin
The economic and human consequences of extreme precipitation and the related flooding of urban areas have increased rapidly over the past decades. Some of the key factors that affect the risks to urban areas include climate change, the densification of assets within cities and the general expansion of urban areas. In this paper, we examine and compare quantitatively the impact of climate change and recent urban development patterns on the exposure of four European cities to pluvial flooding. In particular, we investigate the degree to which pluvial floods of varying severity and in different geographical locations are influenced to the same extent by changes in urban land cover and climate change. We have selected the European cities of Odense, Vienna, Strasbourg and Nice for analyses to represent different climatic conditions, trends in urban development and topographical characteristics. We develop and apply a combined remote-sensing and flood-modelling approach to simulate the extent of pluvial flooding for a range of extreme precipitation events for historical (1984) and present-day (2014) urban land cover and for two climate-change scenarios (i.e. representative concentration pathways, RCP 4.5 and RCP 8.5). Changes in urban land cover are estimated using Landsat satellite imagery for the period 1984-2014. We combine the remote-sensing analyses with regionally downscaled estimates of precipitation extremes of current and expected future climate to enable 2-D overland flow simulations and flood-hazard assessments. The individual and combined impacts of urban development and climate change are quantified by examining the variations in flooding between the different simulations along with the corresponding uncertainties. In addition, two different assumptions are examined with regards to the development of the capacity of the urban drainage system in response to urban development and climate change. In the stationary approach, the capacity resembles present
Stanzel, Ph; Haberl, U; Nachtnebel, H P
Hydrological models for flood forecasting in Alpine basins need accurate representation of snow accumulation and snow melt processes. A continuous, semi-distributed rainfall-runoff model with snow modelling procedures using only precipitation and temperature as input is presented. Simulation results from an application in an Alpine Danube tributary watershed are shown and evaluated with snow depth measurements and MODIS remote sensing snow cover information. Seasonal variations of runoff due to snow melt were simulated accurately. Evaluation of simulated snow depth and snow covered area showed strengths and limitations of the model and allowed an assessment of input data quality. MODIS snow cover images were found to be valuable sources of information for hydrological modelling in alpine areas, where ground observations are scarce.
Stanzel, Ph; Haberl, U; Nachtnebel, H P [Institute of Water Management, Hydrology and Hydraulic Engineering, University of Natural Resources and Applied Life Sciences, Muthgasse 18, 1190 Vienna (Austria)], E-mail: email@example.com
Hydrological models for flood forecasting in Alpine basins need accurate representation of snow accumulation and snow melt processes. A continuous, semi-distributed rainfall-runoff model with snow modelling procedures using only precipitation and temperature as input is presented. Simulation results from an application in an Alpine Danube tributary watershed are shown and evaluated with snow depth measurements and MODIS remote sensing snow cover information. Seasonal variations of runoff due to snow melt were simulated accurately. Evaluation of simulated snow depth and snow covered area showed strengths and limitations of the model and allowed an assessment of input data quality. MODIS snow cover images were found to be valuable sources of information for hydrological modelling in alpine areas, where ground observations are scarce.
Shim, J. B.; Won, C. Y.; Park, J.; Lee, K.
Korea experiences frequent flood disasters, which cause considerable economic losses and damages to towns and farms. Especially, a regional torrential storm is about 98.5mm/hr on September 21, 2010 in Seoul. The storm exceeds the capacity of urban drainage system of 75mm/hr, and 9,419 houses. How to monitor and control the urban flood disasters is an important issue in Korea. To mitigate the flood damage, a customizing system was developed to estimate urban floods and inundation using by integrating drainage system data and river information database which are managed by local governments and national agencies. In the case of Korean urban city, there are a lot of detention ponds and drainage pumping stations on end of drainage system and flow is going into river. The drainage pumping station, it is very important hydraulic facility for flood control between river and drainage system. So, it is possible to occur different patterns of flood inundation according to operation rule of drainage pumping station. A flood disaster is different damage as how to operate drainage pumping station and plan operation rule.
Schaer, Caroline; Hahonou, Eric Komlavi
Disastrous and recurring floods have impacted West African urban centres over the last decade, accentuating already existing vulnerabilities in poor neighbourhoods. Climate change-induced changing weather patterns and more extreme weather events are only part of the explanation for this situation......, as large segments of the urban population in West Africa are not offered the public services, infrastructure and protective regulations needed in order to respond to floods. Through an empirically grounded approach, the article shows that the ability to respond to floods is formed largely outside the realm....... The article concludes that weak state capacity is not equivalent to non-existent of ungoverned collective services linked to floods. While flood response service delivery through co-production, may constitute the best available options in a context of poor resources, because of the negotiated character...
Tanguy, Marion; Bernier, Monique; Chokmani, Karem
the many efforts recently done toward the improvements of the accuracy of the processing algorithms for flood detection in urban areas with high resolution SAR imagery, these algorithms still encounter difficulties to detect urban flooded pixels with precision. The difficulties do not seem to be only ascribable to the choice of SAR image processing methods, but can also be imputed to the limitations of the SAR imaging technique itself in urban areas. We propose a fully automatic and effective approach for near-real time delineation of urban and rural flooded areas, which combines the capacity of SAR imagery to detect open water areas, and explicit hydrodynamic characteristics of the region affected by the flood, expressed through flood recurrence interval data. This innovative approach has been tested with RADARSAT-2 Fine and Ultrafine Mode images acquired during the 2011 Richelieu River flooding, in Canada. It proved successful in accurately delineating flooding in urban and rural areas, with a RMSE inferior to 2 pixels.
Buenrostro, O; Bocco, G; Vence, J
Based on a study of the composition of urban solid waste (USW) and of socioeconomic variables in Morelia, Mexico, generation rates were estimated. In addition, the generation of residential solid waste (RSW) and nonresidential solid waste (NRSW) was forecasted by means of a multiple linear regression (MLR) analysis. For residential sources, the independent variables analyzed were monthly wages, persons per dwelling, age, and educational level of the heads of the household. For nonresidential sources, variables analyzed were number of employees, area of facilities, number of working days, and working hours per day. The forecasted values for residential waste were similar to those observed. This approach may be applied to areas in which available data are scarce, and in which there is an urgent need for the planning of adequate management of USW.
Apel, H.; Trepat, O. M.; Hung, N. N.; Chinh, D. T.; Merz, B.; Dung, N. V.
Many urban areas experience both fluvial and pluvial floods, because locations next to rivers are preferred settlement areas, and the predominantly sealed urban surface prevents infiltration and facilitates surface inundation. The latter problem is enhanced in cities with insufficient or non-existent sewer systems. While there are a number of approaches to analyse either fluvial or pluvial flood hazard, studies of combined fluvial and pluvial flood hazard are hardly available. Thus this study aims at the analysis of fluvial and pluvial flood hazard individually, but also at developing a method for the analysis of combined pluvial and fluvial flood hazard. This combined fluvial-pluvial flood hazard analysis is performed taking Can Tho city, the largest city in the Vietnamese part of the Mekong Delta, as example. In this tropical environment the annual monsoon triggered floods of the Mekong River can coincide with heavy local convective precipitation events causing both fluvial and pluvial flooding at the same time. Fluvial flood hazard was estimated with a copula based bivariate extreme value statistic for the gauge Kratie at the upper boundary of the Mekong Delta and a large-scale hydrodynamic model of the Mekong Delta. This provided the boundaries for 2-dimensional hydrodynamic inundation simulation for Can Tho city. Pluvial hazard was estimated by a peak-over-threshold frequency estimation based on local rain gauge data, and a stochastic rain storm generator. Inundation was simulated by a 2-dimensional hydrodynamic model implemented on a Graphical Processor Unit (GPU) for time-efficient flood propagation modelling. All hazards - fluvial, pluvial and combined - were accompanied by an uncertainty estimation considering the natural variability of the flood events. This resulted in probabilistic flood hazard maps showing the maximum inundation depths for a selected set of probabilities of occurrence, with maps showing the expectation (median) and the uncertainty by
Macchia, S.; Gallo, E.; Claps, P.
In the last decade, the diffusion of mobile telephones and ond of low-cost digital cameras have changed the public approach to catastrophes. As regards floods, it has become widespread the availability of images and videos taken in urban areas. Searching into Youtube or Youreporter, for example, one can understand how often citizen are considering to report even scary events. Nowadays these amateurs videos are often used in news world reports, which often increase or dampen the public perception of flood risk. More importantly, these amateur videos can play a crucial role in a didactic and technical representation of media flooding problems. The question so arise: why don't use the amateur videos for civil protection purposes? This work shows a new way to use flood images and videos to obtain technical data and spread safety information. Specifically, we show how to determine the height and speed of water flow, which have been achieved in some places during Genoa flood - 4th November 2011 - For this event we have downloaded more than 50 videos from different websites, where the authors have provided information about the time of recording, the geographical coordinates and the height above ground of the point of recording. The support by Google tools, such as Google maps and StreetWiew © has allowed us to geographically locate the recording points, so to put together shots and slides necessary to put together a whole reconstruction of the event. Future research will be in the direction of using these videos to generate a tool for the Google platforms, in order to address an easily achievable, yet accurate, information to the public, so to warn people on how to behave in front of imminent floods.
Kaspersen, Per Skougaard; Høegh Ravn, Nanna; Arnbjerg-Nielsen, Karsten
The economic and human consequences of extreme precipitation and the related flooding of urban areas have increased rapidly over the past decades. Some of the key factors that affect the risks to urban areas include climate change, the densification of assets within cities and the general expansion...
Full Text Available Climate change and urbanization have led to an increase in the frequency of extreme water related events such as flooding, which has negative impacts on the environment, economy and human health. With respect to the latter, our understanding of the interrelationship between flooding, urban surface water and human health is still very limited. More in-depth research in this area is needed to further strengthen the process of planning and implementation of responses to mitigate the negative health impacts of flooding in urban areas. The objective of this paper is to assess the state of the research on the interrelationship between surface water quality, flood water quality and human health in urban areas based on the published literature. These insights will be instrumental in identifying and prioritizing future research needs in this area. In this study, research publications in the domain of urban flooding, surface water quality and human health were collated using keyword searches. A detailed assessment of these publications substantiated the limited number of publications focusing on the link between flooding and human health. There was also an uneven geographical distribution of the study areas, as most of the studies focused on developed countries. A few studies have focused on developing countries, although the severity of water quality issues is higher in these countries. The study also revealed a disparity of research in this field across regions in China as most of the studies focused on the populous south-eastern region of China. The lack of studies in some regions has been attributed to the absence of flood water quality monitoring systems which allow the collection of real-time water quality monitoring data during flooding in urban areas. The widespread implementation of cost effective real-time water quality monitoring systems which are based on the latest remote or mobile phone based data acquisition techniques is recommended
Full Text Available Optimal control of reservoirs is a challenging task due to conflicting objectives, complex system structure, and uncertainties in the system. Real time control decisions suffer from streamflow forecast uncertainty. This study aims to use Probabilistic Streamflow Forecasts (PSFs having a lead-time up to 48 h as input for the recurrent reservoir operation problem. A related technique for decision making is multi-stage stochastic optimization using scenario trees, referred to as Tree-based Model Predictive Control (TB-MPC. Deterministic Streamflow Forecasts (DSFs are provided by applying random perturbations on perfect data. PSFs are synthetically generated from DSFs by a new approach which explicitly presents dynamic uncertainty evolution. We assessed different variables in the generation of stochasticity and compared the results using different scenarios. The developed real-time hourly flood control was applied to a test case which had limited reservoir storage and restricted downstream condition. According to hindcasting closed-loop experiment results, TB-MPC outperforms the deterministic counterpart in terms of decreased downstream flood risk according to different independent forecast scenarios. TB-MPC was also tested considering different number of tree branches, forecast horizons, and different inflow conditions. We conclude that using synthetic PSFs in TB-MPC can provide more robust solutions against forecast uncertainty by resolution of uncertainty in trees.
Quilty, J.; Adamowski, J. F.
Urban water supply systems are often stressed during seasonal outdoor water use as water demands related to the climate are variable in nature making it difficult to optimize the operation of the water supply system. Urban water demand forecasts (UWD) failing to include meteorological conditions as inputs to the forecast model may produce poor forecasts as they cannot account for the increase/decrease in demand related to meteorological conditions. Meteorological records stochastically simulated into the future can be used as inputs to data-driven UWD forecasts generally resulting in improved forecast accuracy. This study aims to produce data-driven UWD forecasts for two different Canadian water utilities (Montreal and Victoria) using machine learning methods by first selecting historical UWD and meteorological records derived from a stochastic weather generator using nonlinear input variable selection. The nonlinear input variable selection methods considered in this work are derived from the concept of conditional mutual information, a nonlinear dependency measure based on (multivariate) probability density functions and accounts for relevancy, conditional relevancy, and redundancy from a potential set of input variables. The results of our study indicate that stochastic weather inputs can improve UWD forecast accuracy for the two sites considered in this work. Nonlinear input variable selection is suggested as a means to identify which meteorological conditions should be utilized in the forecast.
Aroca-Jimenez, Estefania; Bodoque, Jose Maria; Garcia, Juan Antonio; Diez-Herrero, Andres
Among the natural hazards, flash flooding is the leading cause of weather-related deaths. Flood risk management (FRM) in this context requires a comprehensive assessment of the social risk component. In this regard, integrated social vulnerability (ISV) can incorporate spatial distribution and contribution and the combined effect of exposure, sensitivity and resilience to total vulnerability, although these components are often disregarded. ISV is defined by the demographic and socio-economic characteristics that condition a population's capacity to cope with, resist and recover from risk and can be expressed as the integrated social vulnerability index (ISVI). This study describes a methodological approach towards constructing the ISVI in urban areas prone to flash flooding in Castilla y León (Castile and León, northern central Spain, 94 223 km2, 2 478 376 inhabitants). A hierarchical segmentation analysis (HSA) was performed prior to the principal components analysis (PCA), which helped to overcome the sample size limitation inherent in PCA. ISVI was obtained from weighting vulnerability factors based on the tolerance statistic. In addition, latent class cluster analysis (LCCA) was carried out to identify spatial patterns of vulnerability within the study area. Our results show that the ISVI has high spatial variability. Moreover, the source of vulnerability in each urban area cluster can be identified from LCCA. These findings make it possible to design tailor-made strategies for FRM, thereby increasing the efficiency of plans and policies and helping to reduce the cost of mitigation measures.
Full Text Available We analyzed simultaneous observation data with ground-based synthetic aperture radar (GB-SAR and airborne SAR (PiSAR over a flood test site at which a simple house was constructed in a field. The PiSAR σ∘ under flood condition was 0.9 to 3.4 dB higher than that under nonflood condition. GB-SAR gives high spatial resolution as we could identify a single scattering component and a double bounce component from the house. GB-SAR showed that the σ∘ difference between the flooding and nonflooding conditions came from the double bounce scattering. We also confirm that the entropy is a sensitive parameter in the eigenvalue decomposition parameters, if the scattering process is dominated by the double bounce scattering. We conclude that σ∘ and entropy are a good parameter to be used to detect flooding, not only in agricultural and forest regions, but also in urban areas. We also conclude that GB-SAR is a powerful tool to supplement satellite and airborne observation, which has a relatively low spatial resolution.
Full Text Available Abstract We analyzed simultaneous observation data with ground-based synthetic aperture radar (GB-SAR and airborne SAR (PiSAR over a flood test site at which a simple house was constructed in a field. The PiSAR under flood condition was 0.9 to 3.4 dB higher than that under nonflood condition. GB-SAR gives high spatial resolution as we could identify a single scattering component and a double bounce component from the house. GB-SAR showed that the difference between the flooding and nonflooding conditions came from the double bounce scattering. We also confirm that the entropy is a sensitive parameter in the eigenvalue decomposition parameters, if the scattering process is dominated by the double bounce scattering. We conclude that and entropy are a good parameter to be used to detect flooding, not only in agricultural and forest regions, but also in urban areas. We also conclude that GB-SAR is a powerful tool to supplement satellite and airborne observation, which has a relatively low spatial resolution.
Full Text Available This paper deals with the failure risk of masonry constructions under the effect of floods. It is developed within a probabilistic framework, with loads and resistances considered as random variables. Two complementary approaches have been investigated for this purpose:
– a global approach based on combined effects of several governing parameters with individual weighted contribution (material quality and geometry, presence and distance between columns, beams, openings, resistance of the soil and its slope. . .,
– and a reliability method using the failure mechanism of masonry walls standing out-plane pressure.
The evolution of the probability of failure of masonry constructions according to the flood water level is analysed.
The analysis of different failure probability scenarios for masonry walls is conducted to calibrate the influence of each "vulnerability governing parameter" in the global approach that is widely used in risk assessment at the urban or regional scale.
The global methodology is implemented in a GIS that provides the spatial distribution of damage risk for different flood scenarios. A real case is considered for the simulations, i.e. Cheffes sur Sarthe (France, for which the observed river discharge, the hydraulic load according to the Digital Terrain Model, and the structural resistance are considered as random variables. The damage probability values provided by both approaches are compared. Discussions are also developed about reduction and mitigation of the flood disaster at various scales (set of structures, city, region as well as resilience.
Gangodagamage, C.; Li, Z.; Maitaria, K.; Islam, M.; Ito, T.; Dhondia, J.
Floods claim more lives and damage more property than any other category of natural disaster in the Continental United States. A system that can demarcate local flood boundaries dynamically could help flood prone communities prepare for and even prevent from catastrophic flood events. Lateral distance from the centerline of the river to the right and left floodplains for the water levels coming out of the models at each grid location have not been properly integrated with the national hydrography dataset (NHDPlus). The NHDPlus dataset represents the stream network with feature classes such as rivers, tributaries, canals, lakes, ponds, dams, coastlines, and stream gages. The NHDPlus dataset consists of approximately 2.7 million river reaches defining how surface water drains to the ocean. These river reaches have upstream and downstream nodes and basic parameters such as flow direction, drainage area, reach slope etc. We modified an existing algorithm (Gangodagamage et al., 2007) to provide lateral distance from the centerline of the river to the right and left floodplains for the flows simulated by models. Previous work produced floodplain boundaries for static river stages (i.e. 3D metric: distance along the main stem, flow depth, lateral distance from river center line). Our new approach introduces the floodplain boundary for variable water levels at each reach with the fourth dimension, time. We use modeled flows from WRF-Hydro and demarcate the right and left lateral boundaries of inundation dynamically by appropriately mapping discharges into hydraulically corrected stages. Backwater effects from the mainstem to tributaries are considered and proper corrections are applied for the tributary inundations. We obtained river stages by optimizing reach level channel parameters using newly developed stream flow routing algorithm. Non uniform inundations are mapped at each NHDplus reach (upstream and downstream nodes) and spatial interpolation is carried out on a
Yokoi, S.; Tsuruoka, H.; Nanjo, K.; Hirata, N.
Collaboratory for the Study of Earthquake Predictability (CSEP) is a global project on earthquake predictability research. The final goal of this project is to search for the intrinsic predictability of the earthquake rupture process through forecast testing experiments. The Earthquake Research Institute, the University of Tokyo joined CSEP and started the Japanese testing center called as CSEP-Japan. This testing center provides an open access to researchers contributing earthquake forecast models applied to Japan. Now more than 100 earthquake forecast models were submitted on the prospective experiment. The models are separated into 4 testing classes (1 day, 3 months, 1 year and 3 years) and 3 testing regions covering an area of Japan including sea area, Japanese mainland and Kanto district. We evaluate the performance of the models in the official suite of tests defined by CSEP. The total number of experiments was implemented for approximately 300 rounds. These results provide new knowledge concerning statistical forecasting models. We started a study for constructing a 3-dimensional earthquake forecasting model for Kanto district in Japan based on CSEP experiments under the Special Project for Reducing Vulnerability for Urban Mega Earthquake Disasters. Because seismicity of the area ranges from shallower part to a depth of 80 km due to subducting Philippine Sea plate and Pacific plate, we need to study effect of depth distribution. We will develop models for forecasting based on the results of 2-D modeling. We defined the 3D - forecasting area in the Kanto region with test classes of 1 day, 3 months, 1 year and 3 years, and magnitudes from 4.0 to 9.0 as in CSEP-Japan. In the first step of the study, we will install RI10K model (Nanjo, 2011) and the HISTETAS models (Ogata, 2011) to know if those models have good performance as in the 3 months 2-D CSEP-Japan experiments in the Kanto region before the 2011 Tohoku event (Yokoi et al., in preparation). We use CSEP
Full Text Available A spatial optimization framework has been developed to help urban areas mitigate climate risks such as flooding and to curb resource use and greenhouse gas emissions. Measures required to address these issues often conflict with each other, for example more compact cities typically use less energy for transportation but increase runoff from high intensity rainfall events. Balancing potential trade-offs and maximizing synergies between these risks and vulnerabilities is therefore a multi-dimensional, spatial, challenge for urban planners. A spatial optimization framework is used to optimize the following objectives to minimize: (1 risk from heat waves; (2 risk from flooding; (3 the distance of new development to the current central business district; (4 urban sprawl to prevent increased travel costs; and (5 the development of green-space. The framework is applied to a real case study in the North East of England. From an initial configuration, alternative spatial configurations are tested against these objectives and the spatial pattern is evolved over successive generations to search for spatially optimum configurations. The resulting solutions provide planners with a range of robust spatial development patterns known to be best trade-offs which mitigate conflicts between risk and sustainability objectives.
Gangodagamage, C.; Li, Z.; Adams, T.; Ito, T.; Maitaria, K.; Islam, M.; Dhondia, J.
Floods claim more lives and damage more property than any other category of natural disaster in the Continental U.S. A system that can demarcate local flood boundaries dynamically could help flood prone communities prepare for and even prevent from catastrophic flood events. Lateral distance from the centerline of the river to the right and left floodplains for the water levels coming out of the models at each grid location have not been properly integrated with the national hydrography dataset (NHDPlus). The NHDPlus dataset represents the stream network with feature classes such as rivers, tributaries, canals, lakes, ponds, dams, coastlines, and stream gages. The NHDPlus dataset consists of approximately 2.7 million river reaches defining how surface water drains to the ocean. These river reaches have upstream and downstream nodes and basic parameters such as flow direction, drainage area, reach slope etc. We modified an existing algorithm (Gangodagamage et al., 2007, 2011) to provide lateral distance from the centerline of the river to the right and left floodplains for the flows simulated by models. Previous work produced floodplain boundaries for static river stages (i.e. 3D metric: distance along the main stem, flow depth, lateral distance from river center line). Our new approach introduces the floodplain boundary for variable water levels with the fourth dimension, time. We use modeled flows from WRFHydro and demarcate the right and left lateral boundaries of inundation dynamically. This approach dynamically integrates with high resolution models (e.g., hourly and ~ 1 km spatial resolution) that are developed from recent advancements in high computational power with ground based measurements (e.g., Fluxnet), lateral inundation vectors (direction and spatial extent) derived from multi-temporal remote sensing data (e.g., LiDAR, WorldView 2, Landsat, ASTER, MODIS), and improved representations of the physical processes through multi-parameterizations. Our
Apel, Heiko; Martínez Trepat, Oriol; Nghia Hung, Nguyen; Thi Chinh, Do; Merz, Bruno; Viet Dung, Nguyen
Many urban areas experience both fluvial and pluvial floods, because locations next to rivers are preferred settlement areas and the predominantly sealed urban surface prevents infiltration and facilitates surface inundation. The latter problem is enhanced in cities with insufficient or non-existent sewer systems. While there are a number of approaches to analyse either a fluvial or pluvial flood hazard, studies of a combined fluvial and pluvial flood hazard are hardly available. Thus this study aims to analyse a fluvial and a pluvial flood hazard individually, but also to develop a method for the analysis of a combined pluvial and fluvial flood hazard. This combined fluvial-pluvial flood hazard analysis is performed taking Can Tho city, the largest city in the Vietnamese part of the Mekong Delta, as an example. In this tropical environment the annual monsoon triggered floods of the Mekong River, which can coincide with heavy local convective precipitation events, causing both fluvial and pluvial flooding at the same time. The fluvial flood hazard was estimated with a copula-based bivariate extreme value statistic for the gauge Kratie at the upper boundary of the Mekong Delta and a large-scale hydrodynamic model of the Mekong Delta. This provided the boundaries for 2-dimensional hydrodynamic inundation simulation for Can Tho city. The pluvial hazard was estimated by a peak-over-threshold frequency estimation based on local rain gauge data and a stochastic rainstorm generator. Inundation for all flood scenarios was simulated by a 2-dimensional hydrodynamic model implemented on a Graphics Processing Unit (GPU) for time-efficient flood propagation modelling. The combined fluvial-pluvial flood scenarios were derived by adding rainstorms to the fluvial flood events during the highest fluvial water levels. The probabilities of occurrence of the combined events were determined assuming independence of the two flood types and taking the seasonality and probability of
Full Text Available This paper evaluates the results of benchmark testing a new inertial formulation of the St. Venant equations, implemented within the LISFLOOD-FP hydraulic model, using different high resolution terrestrial LiDAR data (10 cm, 50 cm and 1 m and roughness conditions (distributed and composite in an urban area. To examine these effects, the model is applied to a hypothetical flooding scenario in Alcester, UK, which experienced surface water flooding during summer 2007. The sensitivities of simulated water depth, extent, arrival time and velocity to grid resolutions and different roughness conditions are analysed. The results indicate that increasing the terrain resolution from 1 m to 10 cm significantly affects modelled water depth, extent, arrival time and velocity. This is because hydraulically relevant small scale topography that is accurately captured by the terrestrial LIDAR system, such as road cambers and street kerbs, is better represented on the higher resolution DEM. It is shown that altering surface friction values within a wide range has only a limited effect and is not sufficient to recover the results of the 10 cm simulation at 1 m resolution. Alternating between a uniform composite surface friction value (n = 0.013 or a variable distributed value based on land use has a greater effect on flow velocities and arrival times than on water depths and inundation extent. We conclude that the use of extra detail inherent in terrestrial laser scanning data compared to airborne sensors will be advantageous for urban flood modelling related to surface water, risk analysis and planning for Sustainable Urban Drainage Systems (SUDS to attenuate flow.
Full Text Available This study adopts rainwater harvesting systems (RWHS into a stormwater runoff management model (SWMM for the spatial design of capacities and quantities of rain barrel for urban flood mitigation. A simulation-optimization model is proposed for effectively identifying the optimal design. First of all, we particularly classified the characteristic zonal subregions for spatial design by using fuzzy C-means clustering with the investigated data of urban roof, land use and drainage system. In the simulation method, a series of regular spatial arrangements specification are designed by using statistical quartiles analysis for rooftop area and rainfall frequency analysis; accordingly, the corresponding reduced flooding circumstances can be simulated by SWMM. Moreover, the most effective solution for the simulation method is identified from the calculated net benefit, which is equivalent to the subtraction of the facility cost from the decreased inundation loss. It serves as the initially identified solution for the optimization model. In the optimization method, backpropagation neural network (BPNN are first applied for developing a water level simulation model of urban drainage systems to substitute for SWMM to conform to newly considered interdisciplinary multi-objective optimization model, and a tabu search-based algorithm is used with the embedded BPNN-based SWMM to optimize the planning solution. The developed method is applied to the Zhong-He District, Taiwan. Results demonstrate that the application of tabu search and the BPNN-based simulation model into the optimization model can effectively, accurately and fast search optimal design considering economic net benefit. Furthermore, the optimized spatial rain barrel design could reduce 72% of inundation losses according to the simulated flood events.
Cherqui, Frédéric; Belmeziti, Ali; Granger, Damien; Sourdril, Antoine; Le Gauffre, Pascal
Flood protection is one of the traditional functions of any drainage system, and it remains a major issue in many cities because of economic and health impact. Heavy rain flooding has been well studied and existing simulation software can be used to predict and improve level of protection. However, simulating minor flooding remains highly complex, due to the numerous possible causes related to operational deficiencies or negligent behaviour. According to the literature, causes of blockages vary widely from one case to another: it is impossible to provide utility managers with effective recommendations on how to improve the level of protection. It is therefore vital to analyse each context in order to define an appropriate strategy. Here we propose a method to represent and assess the flooding risk, using GIS and data gathered during operation and maintenance. Our method also identifies potential management responses. The approach proposed aims to provide decision makers with clear and comprehensible information. Our method has been successfully applied to the Urban Community of Bordeaux (France) on 4895 interventions related to flooding recorded during the 2009-2011 period. Results have shown the relative importance of different issues, such as human behaviour (grease, etc.) or operational deficiencies (roots, etc.), and lead to identify corrective and proactive. This study also confirms that blockages are not always directly due to the network itself and its deterioration. Many causes depend on environmental and operating conditions on the network and often require collaboration between municipal departments in charge of roads, green spaces, etc. Copyright © 2015 Elsevier B.V. All rights reserved.
Lee, G.; Miller, A. J.
Urban stream restoration efforts are commonly undertaken to combat channel degradation and restore natural stream hydrology. We examine changes in flood patterns along an approximately 1.5-mile reach of Minebank Run, located in Towson, MD, by comparing pre-restoration morphology from surveys conducted in 2001, post-restoration morphology in 2007, and current conditions in 2017 following damage to the restoration project from persistent flooding. Hydraulic modeling was conducted in HEC-RAS 2D using three alternative scenarios: 1) topographic contours from a 2001 survey of pre-restoration topography combined with 2005 LiDAR, 2) 2007 survey combined with 2005 LiDAR data representing the post-restoration channel morphology, and 3) a March 2017 DEM of current channel conditions. The 2017 DEM was created using Structure from Motion (SfM) from high resolution 4K video collected via Unmanned Aerial Vehicle (UAV) flights at a resolution of 0.05 meters. Flood hydrographs from a USGS stream gage located within the study reach as well as a simulated hydrograph of the 100-year storm event were routed through the pre-restoration, post-restoration, and current modeled terrain and analyzed for changes in water-surface elevation and depth, inundation extent, 2-d velocity fields, and translation vs. attenuation of the flood wave to assess the net impact on potential flood hazards. In addition, our study demonstrates that SfM is a quick and inexpensive method for collecting topographic data for hydrologic modeling, assessing stream characteristics including channel bed roughness, and for examining short term changes of channel morphology at a very fine scale.
Longo, Elisa; Tito Aronica, Giuseppe; Di Baldassarre, Giuliano; Mukolwe, Micah
Flooding is one of the most impactful natural hazards. In particular, by looking at the data of damages from natural hazards in Europe collected in the International Disaster Database (EM-DAT) one can see a significant increase over the past four decades of both frequency of floods and associated economic damages. Similarly, dramatic trends are also found by analyzing other types of flood losses, such as the number of people affected by floods, homeless, injured or killed. To deal with the aforementioned increase of flood risk, more and more efforts are being made to promote integrated flood risk management, for instance, at the end of 2007, the European Community (EC) issued the Flood Directive (F.D.) 2007/60/EC. One of the major innovations was that the F.D. 2007/60/C requires Member State to carry out risk maps and then take appropriate measures to reduce the evaluated risk. The main goal of this research was to estimate flood damaging using a computer code based on a recently developed method (KULTURisk, www.kulturisk.eu) and to compare the estimated damage with the observed one. The study area was the municipality of Eilenburg, which in 2002 was subjected to a destructive flood event. Were produced flood damage maps with new procedures (e.g. KULTURisk) and compared the estimates with observed data. This study showed the possibility to extend the lesson learned with the Eilenburg case study in other similar contexts. The outcomes of this test provided interesting insights about the flood risk mapping, which are expected to contribute to raise awareness to the flooding issues,to plan (structural and/or non-structural) measures of flood risk reduction and to support better land-use and urban planning.
Noh, S. J.; Lee, S.; Lee, J.; Seo, D. J.
Floods occur most frequently among all natural hazards, often causing widespread economic damage and loss of human lives. In particular, urban flooding is becoming increasingly costly and difficult to manage with a greater concentration of population and assets in urban centers. Despite of known benefits for accurate representation of small scale features and flow interaction among different flow domains, which have significant impact on flood propagation, high-resolution modeling has not been fully utilized due to expensive computation and various uncertainties from model structure, input and parameters. In this study, we assess the potential of hyper-resolution hydrologic-hydraulic modeling using high-resolution radar precipitation and LiDAR data for improved urban flood prediction and hazard mapping. We describe a hyper-resolution 1D-2D coupled urban flood model for pipe and surface flows and evaluate the accuracy of the street-level inundation information produced. For detailed geometric representation of urban areas and for computational efficiency, we use 1 m-resolution topographical data, processed from LiDAR measurements, in conjunction with adaptive mesh refinement. For street-level simulation in large urban areas at grid sizes of 1 to 10 m, a hybrid parallel computing scheme using MPI and openMP is also implemented in a high-performance computing system. The modeling approach developed is applied for the Johnson Creek Catchment ( 40 km2), which makes up the Arlington Urban Hydroinformatics Testbed. In addition, discussion will be given on availability of hyper-resolution simulation archive for improved real-time flood mapping.
Full Text Available Regional heavy rainfall is usually caused by the influence of extreme weather conditions. Instant heavy rainfall often results in the flooding of rivers and the neighboring low-lying areas, which is responsible for a large number of casualties and considerable property loss. The existing precipitation forecast systems mostly focus on the analysis and forecast of large-scale areas but do not provide precise instant automatic monitoring and alert feedback for individual river areas and sections. Therefore, in this paper, we propose an easy method to automatically monitor the flood object of a specific area, based on the currently widely used remote cyber surveillance systems and image processing methods, in order to obtain instant flooding and waterlogging event feedback. The intrusion detection mode of these surveillance systems is used in this study, wherein a flood is considered a possible invasion object. Through the detection and verification of flood objects, automatic flood risk-level monitoring of specific individual river segments, as well as the automatic urban inundation detection, has become possible. The proposed method can better meet the practical needs of disaster prevention than the method of large-area forecasting. It also has several other advantages, such as flexibility in location selection, no requirement of a standard water-level ruler, and a relatively large field of view, when compared with the traditional water-level measurements using video screens. The results can offer prompt reference for appropriate disaster warning actions in small areas, making them more accurate and effective.
Sun, D. C.; Huang, J.; Wang, H. M.; Wang, Z. Q.; Wang, W. Q.
The research of urban flood risk assessment and management are of great academic and practical importance, which has become a widespread concern throughout the world. It’s significant to understand the spatial-temporal distribution of the flood risk before making the risk response measures. In this study, the urban region of Jingdezhen City is selected as the study area. The assessment indicators are selected from four aspects: disaster-causing factors, disaster-pregnant environment, disaster-bearing body and the prevention and mitigation ability, by consideration of the formation process of urban flood risk. And then, a small-scale flood disaster risk assessment model is developed based on Analytic Hierarchy Process(AHP) and Geographic Information System(GIS), and the spatial-temporal distribution of flood risk in Jingdezhen City is analysed. The results show that the risk decreases gradually from the centre line of Changjiang River to the surrounding, and the areas of high flood disaster risk is decreasing from 2010 to 2013 while the risk areas are more concentred. The flood risk of the areas along the Changjiang River is the largest, followed by the low-lying areas in Changjiang District. And the risk is also large in Zhushan District where the population, the industries and commerce are concentrated. The flood risk in the western part of Changjiang District and the north-eastern part of the study area is relatively low. The results can provide scientific support for flood control construction and land development planning in Jingdezhen City.
Rashid, S F
Bangladesh experienced one of the worst floods in recorded history in 1998. This paper focuses on the needs and coping strategies of the urban poor in Dhaka City, which had been very badly affected. The city's roads were completely under water, and most areas were water-logged with drainage and sewage systems blocked. Rising water levels compelled many slum dwellers to move to temporary shelters and relief camps. Women and children were the worst affected. The lack of sanitation facilities and privacy forced women and children to defecate in their own homes. There was an acute scarcity of safe drinking-water, and food prices rose dramatically. Diarrhoea, fever and colds were the most common illnesses affecting the poor. The floods left many of them unemployed, and in some families, the result was increased tension and incidents of domestic violence. In some areas, members felt pressured to repay micro-credit loans. Most NGOs, however, suspended loan repayments. During this period, a committee was set up to co-ordinate and work towards addressing some of the main post-flood problems.
Muis, Sanne; Güneralp, Burak; Jongman, Brenden; Aerts, Jeroen C J H; Ward, Philip J
An accurate understanding of flood risk and its drivers is crucial for effective risk management. Detailed risk projections, including uncertainties, are however rarely available, particularly in developing countries. This paper presents a method that integrates recent advances in global-scale modeling of flood hazard and land change, which enables the probabilistic analysis of future trends in national-scale flood risk. We demonstrate its application to Indonesia. We develop 1000 spatially-explicit projections of urban expansion from 2000 to 2030 that account for uncertainty associated with population and economic growth projections, as well as uncertainty in where urban land change may occur. The projections show that the urban extent increases by 215%-357% (5th and 95th percentiles). Urban expansion is particularly rapid on Java, which accounts for 79% of the national increase. From 2000 to 2030, increases in exposure will elevate flood risk by, on average, 76% and 120% for river and coastal floods. While sea level rise will further increase the exposure-induced trend by 19%-37%, the response of river floods to climate change is highly uncertain. However, as urban expansion is the main driver of future risk, the implementation of adaptation measures is increasingly urgent, regardless of the wide uncertainty in climate projections. Using probabilistic urban projections, we show that spatial planning can be a very effective adaptation strategy. Our study emphasizes that global data can be used successfully for probabilistic risk assessment in data-scarce countries. Copyright © 2015 Elsevier B.V. All rights reserved.
Pérez-Molina, Eduardo; Sliuzas, R.V.; Flacke, J.; Jetten, V.G.
Urban growth may intensify local flooding problems. Understanding the spatially explicit flood consequences of possible future land cover patterns contributes to inform policy for mitigating these impacts. A cellular automata model has been coupled with the openLISEM integrated flood modeling tool
Liu, Dedi; Li, Xiang; Guo, Shenglian
Dynamic control of the flood limiting water level (FLWL) is a valuable and effective way to maximize the benefits from reservoir operation without exceeding the design risk. In order to analyze the impacts of input uncertainty, a Bayesian forecasting system (BFS) is adopted. Applying quantile water...... inflow values and their uncertainties obtained from the BFS, the reservoir operation results from different schemes can be analyzed in terms of benefits, dam safety, and downstream impacts during the flood season. When the reservoir FLWL dynamic control operation is implemented, there are two fundamental......, also deterministic water inflow was tested. The proposed model in the paper emphasizes the importance of analyzing the uncertainties of the water inflow forecasting system for real-time dynamic control of the FLWL for reservoir operation. For the case study, the selected quantile inflow from...
Brian W. Head
Full Text Available In this case study, I examine the quality of decision-making under conditions of rapidly evolving urban water crises, and the adaptive policy challenges of building regional resilience in response to both drought and flood. Like other regions of Australia, Southeast Queensland has been subject to substantial cycles of drought and flood. I draw on resilience literature concerning sustainability, together with governance literature on policy change, to explain the changing awareness of urban water crises and the strategic options available for addressing these crises in this case study. The problem of resilience thinking opens up a number of important questions about the efficacy and adaptability of the policy system. The case provides insights into the interplay between the ways in which problems are framed, the knowledge bases required for planning and decision-making, the collaborative governance processes required for managing complex and rapidly evolving issues, and the overall capacity for policy learning over time. Regional resilience was proclaimed as a policy goal by government, but the practices remained largely anchored in traditional technical frameworks. Centralized investment decisions and governance restructures provoked conflict between levels of government, undermining the capacity of stakeholders to create more consensual approaches to problem-solving and limiting the collective learning that could have emerged.
Full Text Available Coastal storms can take a devastating toll on the public's health. Urban areas like New York City (NYC may be particularly at risk, given their dense population, reliance on transportation, energy infrastructure that is vulnerable to flood damage, and high-rise residential housing, which may be hard-hit by power and utility outages. Climate change will exacerbate these risks in the coming decades. Sea levels are rising due to global warming, which will intensify storm surge. These projections make preparing for the health impacts of storms even more important. We conducted a broad review of the health impacts of US coastal storms to inform climate adaptation planning efforts, with a focus on outcomes relevant to NYC and urban coastal areas, and incorporated some lessons learned from recent experience with Superstorm Sandy. Based on the literature, indicators of health vulnerability were selected and mapped within NYC neighborhoods. Preparing for the broad range of anticipated effects of coastal storms and floods may help reduce the public health burden from these events.
Lane, Kathryn; Charles-Guzman, Kizzy; Wheeler, Katherine; Abid, Zaynah; Graber, Nathan; Matte, Thomas
Coastal storms can take a devastating toll on the public's health. Urban areas like New York City (NYC) may be particularly at risk, given their dense population, reliance on transportation, energy infrastructure that is vulnerable to flood damage, and high-rise residential housing, which may be hard-hit by power and utility outages. Climate change will exacerbate these risks in the coming decades. Sea levels are rising due to global warming, which will intensify storm surge. These projections make preparing for the health impacts of storms even more important. We conducted a broad review of the health impacts of US coastal storms to inform climate adaptation planning efforts, with a focus on outcomes relevant to NYC and urban coastal areas, and incorporated some lessons learned from recent experience with Superstorm Sandy. Based on the literature, indicators of health vulnerability were selected and mapped within NYC neighborhoods. Preparing for the broad range of anticipated effects of coastal storms and floods may help reduce the public health burden from these events.
Full Text Available Water-energy nexus has been a popular topic of rese arch in recent years. The relationships between the demand for water resources and energy are intense and closely connected in urban areas. The primary, secondary, and tertiary industry gross domestic product (GDP, the total population, the urban population, annual precipitation, agricultural and industrial water consumption, tap water supply, the total discharge of industrial wastewater, the daily sewage treatment capacity, total and domestic electricity consumption, and the consumption of coal in industrial enterprises above the designed size were chosen as input indicators. A feedforward artificial neural network model (ANN based on a back-propagation algorithm with two hidden layers was constructed to combine urban water resources with energy demand. This model used historical data from 1991 to 2016 from Wuxi City, eastern China. Furthermore, a multiple linear regression model (MLR was introduced for comparison with the ANN. The results show the following: (a The mean relative error values of the forecast and historical urban water-energy demands are 1.58 % and 2.71%, respectively; (b The predicted water-energy demand value for 2020 is 4.843 billion cubic meters and 47.561 million tons of standard coal equivalent; (c The predicted water-energy demand value in the year 2030 is 5.887 billion cubic meters and 60.355 million tons of standard coal equivalent; (d Compared with the MLR, the ANN performed better in fitting training data, which achieved a more satisfactory accuracy and may provide a reference for urban water-energy supply planning decisions.
James D. Miller
New hydrological insights: There is a lack of nationally research focused on the dual impacts of climate change and urbanisation on flooding and water quality in UK urban areas. This is despite there being a clear acceptance that flood risk is increasing, water quality is generally not meeting desirable levels, and that combined population and climate change projections pose a pressing challenge. The available evidence has been found to be of medium-high confidence that both pressures will result in (i an increase in pluvial and fluvial flood risk, and (ii further reduction in water quality caused by point source pollution and altered flow regimes. Evidence concerning urban groundwater flooding, diffuse pollution and water temperature was found to be more sparse and was ascribed a low-medium confidence that both pressures will further exacerbate existing issues. The confidence ascribed to evidence was also found to reflect the utility of current science for setting policy and urban planning. Recurring factors that limit the utility of evidence for managing the urban environment includes: (i climate change projection uncertainty and suitability, (ii lack of sub-daily projections for storm rainfall, (iii the complexity of managing and modelling the urban environment, and (iv lack of probable national-scale future urban land-use projections. Suitable climate products are increasingly being developed and their application in applied urban research is critical in the wake of a series of extreme flooding events across the UK and timely for providing state-of-the-art evidence on which to base possible future water quality legislation in a post Brexit-WFD era.
Lindquist, E.; Pierce, J. L.
Numerous frameworks and models exist for understanding the dynamics of the public policy process. A policy network approach considers how and why stakeholders and interests pay attention to and engage in policy problems, such as flood control or developing resilient and fire resistant landscapes. Variables considered in this approach include what the relationships are between these stakeholders, how they influence the process and outcomes, communication patterns within and between policy networks, and how networks change as a result of new information, science, or public interest and involvement with the problem. This approach is useful in understanding the creation of natural hazards policy as new information or situations, such as projected climate change impacts, influence and disrupt the policy process and networks. Two significant natural hazard policy networks exist in the semi-arid Treasure Valley region of Southwest Idaho, which includes the capitol city of Boise and the surrounding metropolitan area. Boise is situated along the Boise River and adjacent to steep foothills; this physiographic setting makes Boise vulnerable to both wildfires at the wildland-urban interface (WUI) and flooding. Both of these natural hazards have devastated the community in the past and floods and fires are projected to occur with more frequency in the future as a result of projected climate change impacts in the region. While both hazards are fairly well defined problems, there are stark differences lending themselves to comparisons across their respective networks. The WUI wildfire network is large and well developed, includes stakeholders from all levels of government, the private sector and property owner organizations, has well defined objectives, and conducts promotional and educational activities as part of its interaction with the public in order to increase awareness and garner support for its policies. The flood control policy network, however, is less defined
Full Text Available Urban flooding and thermal stress have become key issues for many cities around the world. With the continuing effects of climate change, these two issues will become more acute and will add to the serious problems already experienced in dense urban areas. Therefore, the sectors of public health and disaster management are in the need of tools that can assess the vulnerability to floods and thermal stress. The present paper deals with the combination of innovative tools to address this challenge. Three cities in different climatic regions with various urban contexts have been selected as the pilot areas to demonstrate these tools. These cities are Tainan (Taiwan, Ayutthaya (Thailand and Groningen (Netherlands. For these cities, flood maps and heat stress maps were developed and used for the comparison analysis. The flood maps produced indicate vulnerable low-lying areas, whereas thermal stress maps indicate open, unshaded areas where high Physiological Equivalent Temperature (PET values (thermal comfort can be expected. The work to date indicates the potential of combining two different kinds of maps to identify and analyse the problem areas. These maps could be further improved and used by urban planners and other stakeholders to assess the resilience and well-being of cities. The work presented shows that the combined analysis of such maps also has a strong potential to be used for the analysis of other challenges in urban dense areas such as air and water pollution, immobility and noise disturbance.
Huang, C.; Hsu, N.
This study imports Low-Impact Development (LID) technology of rainwater catchment systems into a Storm-Water runoff Management Model (SWMM) to design the spatial capacity and quantity of rain barrel for urban flood mitigation. This study proposes a simulation-optimization model for effectively searching the optimal design. In simulation method, we design a series of regular spatial distributions of capacity and quantity of rainwater catchment facilities, and thus the reduced flooding circumstances using a variety of design forms could be simulated by SWMM. Moreover, we further calculate the net benefit that is equal to subtract facility cost from decreasing inundation loss and the best solution of simulation method would be the initial searching solution of the optimization model. In optimizing method, first we apply the outcome of simulation method and Back-Propagation Neural Network (BPNN) for developing a water level simulation model of urban drainage system in order to replace SWMM which the operating is based on a graphical user interface and is hard to combine with optimization model and method. After that we embed the BPNN-based simulation model into the developed optimization model which the objective function is minimizing the negative net benefit. Finally, we establish a tabu search-based algorithm to optimize the planning solution. This study applies the developed method in Zhonghe Dist., Taiwan. Results showed that application of tabu search and BPNN-based simulation model into the optimization model not only can find better solutions than simulation method in 12.75%, but also can resolve the limitations of previous studies. Furthermore, the optimized spatial rain barrel design can reduce 72% of inundation loss according to historical flood events.
An impossibility to foresee in advance the accurate traffic parameters in face of dynamism phenomena in complex transportation system is a one of the major source of uncertainty. The paper presents an approach to robust optimization of logistics vehicle routes in urban areas on the basis of estimated short-term traffic time forecasts in a selected area of the urban road network. The forecast values of optimization parameters have been determined using the spectral analysis model, taking into account the forecast uncertainty degree. The robust counterparts approach of uncertain bi-criteria shortest path problem formulation is used to determining the robust routes for logistics vehicles in the urban network. The uncertainty set is created on the basis of forecast travel times in chosen sections, estimated by means of spectral analysis. The advantages and the characteristics are exemplified in the actual Krakow road network. The obtained data have been compared with classic approach wherein it is assumed that the optimization parameters are certain and accurate. The results obtained in the simulation example indicate that use of forecasting techniques with robust optimization models has a positive impact on the quality of final solutions. (Author)
Saleh, F.; Ramaswamy, V.; Wang, Y.; Georgas, N.; Blumberg, A.; Pullen, J.
Estuarine regions can experience compound impacts from coastal storm surge and riverine flooding. The challenges in forecasting flooding in such areas are multi-faceted due to uncertainties associated with meteorological drivers and interactions between hydrological and coastal processes. The objective of this work is to evaluate how uncertainties from meteorological predictions propagate through an ensemble-based flood prediction framework and translate into uncertainties in simulated inundation extents. A multi-scale framework, consisting of hydrologic, coastal and hydrodynamic models, was used to simulate two extreme flood events at the confluence of the Passaic and Hackensack rivers and Newark Bay. The events were Hurricane Irene (2011), a combination of inland flooding and coastal storm surge, and Hurricane Sandy (2012) where coastal storm surge was the dominant component. The hydrodynamic component of the framework was first forced with measured streamflow and ocean water level data to establish baseline inundation extents with the best available forcing data. The coastal and hydrologic models were then forced with meteorological predictions from 21 ensemble members of the Global Ensemble Forecast System (GEFS) to retrospectively represent potential future conditions up to 96 hours prior to the events. Inundation extents produced by the hydrodynamic model, forced with the 95th percentile of the ensemble-based coastal and hydrologic boundary conditions, were in good agreement with baseline conditions for both events. The USGS reanalysis of Hurricane Sandy inundation extents was encapsulated between the 50th and 95th percentile of the forecasted inundation extents, and that of Hurricane Irene was similar but with caveats associated with data availability and reliability. This work highlights the importance of accounting for meteorological uncertainty to represent a range of possible future inundation extents at high resolution (∼m).
Zhang, Hao; Ma, Wei-Chun; Wang, Xiang-Rong
The purpose of this paper is to examine the linkage between rapid urbanization and flood risk in the hinterlands of the Pearl River Delta, P.R. China. Foshan, a typical hinterland city in the Pearl River Delta region, was selected as a case study. Land use and cover change in Foshan during 1988-2003 was analyzed using remote sensing and geographic information system (GIS) techniques. Furthermore, analysis on historical hydrological data during 1962-2005 was performed. Results show that rapid urbanization has resulted in losses of farmland, forest and shrub since 1988. In addition, in order to compensate or offset the loss of farmland due to rapid urban expansion, more than 30 % of the forest and 20 % of the shrub areas were transformed into farmlands. Inevitably, both the urban and agricultural lands increased the pressure on the drainage systems. Furthermore, over the past decades human activities such as dredging up the floodways, excavating sand and building water facilities in the rivers, significantly changed the hydrological conditions, and therefore impaired the rivers' capacity to buffer floods. Lessons from the Foshan case implied that, in addition to natural processes, human activities driven by socio-economic factors should be considered responsible for the recently increasing level of flood risks. Both economically and environmentally, it is irrational and impractical to encourage encroachment of lands vulnerable to floods. It is also realistic and urgent to effectively prevent and control the adverse ecological consequences of urbanization and economic activities for building their wealth and prominence.
Full Text Available Flooding is a severe natural hazard, which poses a great threat to human life and property, especially in densely-populated urban areas. As one of the fastest developing fields in remote sensing applications, an unmanned aerial vehicle (UAV can provide high-resolution data with a great potential for fast and accurate detection of inundated areas under complex urban landscapes. In this research, optical imagery was acquired by a mini-UAV to monitor the serious urban waterlogging in Yuyao, China. Texture features derived from gray-level co-occurrence matrix were included to increase the separability of different ground objects. A Random Forest classifier, consisting of 200 decision trees, was used to extract flooded areas in the spectral-textural feature space. Confusion matrix was used to assess the accuracy of the proposed method. Results indicated the following: (1 Random Forest showed good performance in urban flood mapping with an overall accuracy of 87.3% and a Kappa coefficient of 0.746; (2 the inclusion of texture features improved classification accuracy significantly; (3 Random Forest outperformed maximum likelihood and artificial neural network, and showed a similar performance to support vector machine. The results demonstrate that UAV can provide an ideal platform for urban flood monitoring and the proposed method shows great capability for the accurate extraction of inundated areas.
Saleh, F.; Ramaswamy, V.; Georgas, N.; Blumberg, A. F.; Wang, Y.
Advances in computational resources and modeling techniques are opening the path to effectively integrate existing complex models. In the context of flood prediction, recent extreme events have demonstrated the importance of integrating components of the hydrosystem to better represent the interactions amongst different physical processes and phenomena. As such, there is a pressing need to develop holistic and cross-disciplinary modeling frameworks that effectively integrate existing models and better represent the operative dynamics. This work presents a novel Hydrologic-Hydraulic-Hydrodynamic Ensemble (H3E) flood prediction framework that operationally integrates existing predictive models representing coastal (New York Harbor Observing and Prediction System, NYHOPS), hydrologic (US Army Corps of Engineers Hydrologic Modeling System, HEC-HMS) and hydraulic (2-dimensional River Analysis System, HEC-RAS) components. The state-of-the-art framework is forced with 125 ensemble meteorological inputs from numerical weather prediction models including the Global Ensemble Forecast System, the European Centre for Medium-Range Weather Forecasts (ECMWF), the Canadian Meteorological Centre (CMC), the Short Range Ensemble Forecast (SREF) and the North American Mesoscale Forecast System (NAM). The framework produces, within a 96-hour forecast horizon, on-the-fly Google Earth flood maps that provide critical information for decision makers and emergency preparedness managers. The utility of the framework was demonstrated by retrospectively forecasting an extreme flood event, hurricane Sandy in the Passaic and Hackensack watersheds (New Jersey, USA). Hurricane Sandy caused significant damage to a number of critical facilities in this area including the New Jersey Transit's main storage and maintenance facility. The results of this work demonstrate that ensemble based frameworks provide improved flood predictions and useful information about associated uncertainties, thus
Full Text Available The 'Montserrat-2000' severe flash flood event which occurred over Catalonia on 9 and 10 June 2000 is analyzed. Strong precipitation was generated by a mesoscale convective system associated with the development of a cyclone. The location of heavy precipitation depends on the position of the cyclone, which, in turn, is found to be very sensitive to various model characteristics and initial conditions. Numerical simulations of this case study using the hydrostatic BOLAM and the non-hydrostatic MOLOCH models are performed in order to test the effects of different formulations of the boundary layer parameterization: a modified version of the Louis (order 1 model and a custom version of the E-ℓ (order 1.5 model. Both of them require a diagnostic formulation of the mixing length, but the use of the turbulent kinetic energy equation in the E-ℓ model allows to represent turbulence history and non-locality effects and to formulate a more physically based mixing length. The impact of the two schemes is different in the two models. The hydrostatic model, run at 1/5 degree resolution, is less sensitive, but the quantitative precipitation forecast is in any case unsatisfactory in terms of localization and amount. Conversely, the non-hydrostatic model, run at 1/50 degree resolution, is capable of realistically simulate timing, position and amount of precipitation, with the apparently superior results obtained with the E-ℓ parameterization model.
Feaster, Toby D.; Gotvald, Anthony J.; Weaver, J. Curtis
Reliable estimates of the magnitude and frequency of floods are essential for such things as the design of transportation and water-conveyance structures, Flood Insurance Studies, and flood-plain management. The flood-frequency estimates are particularly important in densely populated urban areas. A multistate approach was used to update methods for determining the magnitude and frequency of floods in urban and small, rural streams that are not substantially affected by regulation or tidal fluctuations in Georgia, South Carolina, and North Carolina. The multistate approach has the advantage over a single state approach of increasing the number of stations available for analysis, expanding the geographical coverage that would allow for application of regional regression equations across state boundaries, and building on a previous flood-frequency investigation of rural streamflow-gaging stations (streamgages) in the Southeastern United States. In addition, streamgages from the inner Coastal Plain of New Jersey were included in the analysis. Generalized least-squares regression techniques were used to generate predictive equations for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probability flows for urban and small, rural ungaged basins for three hydrologic regions; the Piedmont-Ridge and Valley, Sand Hills, and Coastal Plain. Incorporation of urban streamgages from New Jersey also allowed for the expansion of the applicability of the predictive equations in the Coastal Plain from 2.1 to 53.5 square miles. Explanatory variables in the regression equations included drainage area (DA) and percent of impervious area (IA) for the Piedmont-Ridge and Valley region; DA and percent of developed land for the Sand Hills; and DA, IA, and 24-hour, 50-year maximum precipitation for the Coastal Plain. An application spreadsheet also was developed that can be used to compute the flood-frequency estimates along with the 95-percent prediction
Full Text Available A hydrological model for real time flood forecasting to Civil Protection services requires reliability and rapidity. At present, computational capabilities overcome the rapidity needs even when a fully distributed hydrological model is adopted for a large river catchment as the Upper Po river basin closed at Ponte Becca (nearly 40 000 km2. This approach allows simulating the whole domain and obtaining the responses of large as well as of medium and little sized sub-catchments. The FEST-WB hydrological model (Mancini, 1990; Montaldo et al., 2007; Rabuffetti et al., 2008 is implemented. The calibration and verification activities are based on more than 100 flood events, occurred along the main tributaries of the Po river in the period 2000–2003. More than 300 meteorological stations are used to obtain the forcing fields, 10 cross sections with continuous and reliable discharge time series are used for calibration while verification is performed on about 40 monitored cross sections. Furthermore meteorological forecasting models are used to force the hydrological model with Quantitative Precipitation Forecasts (QPFs for 36 h horizon in "operational setting" experiments. Particular care is devoted to understanding how QPF affects the accuracy of the Quantitative Discharge Forecasts (QDFs and to assessing the QDF uncertainty impact on the warning system reliability. Results are presented either in terms of QDF and of warning issues highlighting the importance of an "operational based" verification approach.
Yin, Jie; Yu, Dapeng; Yin, Zhane; Liu, Min; He, Qing
Urban pluvial flood are attracting growing public concern due to rising intense precipitation and increasing consequences. Accurate risk assessment is critical to an efficient urban pluvial flood management, particularly in transportation sector. This paper describes an integrated methodology, which initially makes use of high resolution 2D inundation modeling and flood depth-dependent measure to evaluate the potential impact and risk of pluvial flash flood on road network in the city center of Shanghai, China. Intensity-Duration-Frequency relationships of Shanghai rainstorm and Chicago Design Storm are combined to generate ensemble rainfall scenarios. A hydrodynamic model (FloodMap-HydroInundation2D) is used to simulate overland flow and flood inundation for each scenario. Furthermore, road impact and risk assessment are respectively conducted by a new proposed algorithm and proxy. Results suggest that the flood response is a function of spatio-temporal distribution of precipitation and local characteristics (i.e. drainage and topography), and pluvial flash flood is found to lead to proportionate but nonlinear impact on intra-urban road inundation risk. The approach tested here would provide more detailed flood information for smart management of urban street network and may be applied to other big cities where road flood risk is evolving in the context of climate change and urbanization.
Kochilakis, Giorgos; Poursanidis, Dimitris; Chrysoulakis, Nektarios; Varella, Vassiliki; Kotroni, Vassiliki; Eftychidis, Giorgos; Lagouvardos, Kostas; Papathanasiou, Chrysoula; Karavokyros, George; Aivazoglou, Maria; Makropoulos, Christos; Mimikou, Maria
A web-based Decision Support System, named FLIRE DSS, for combined forest fire control and planning as well as flood risk management, has been developed and is presented in this paper. State of the art tools and models have been used in order to enable Civil Protection agencies and local stakeholders to take advantage of the web based DSS without the need of local installation of complex software and their maintenance. Civil protection agencies can predict the behavior of a fire event using real time data and in such a way plan its efficient elimination. Also, during dry periods, agencies can implement "what-if" scenarios for areas that are prone to fire and thus have available plans for forest fire management in case such scenarios occur. Flood services include flood maps and flood-related warnings and become available to relevant authorities for visualization and further analysis on a daily basis. When flood warnings are issued, relevant authorities may proceed to efficient evacuation planning for the areas that are likely to flood and thus save human lives. Real-time weather data from ground stations provide the necessary inputs for the calculation of the fire model in real-time, and a high resolution weather forecast grid supports flood modeling as well as the development of "what-if" scenarios for the fire modeling. All these can be accessed by various computer sources including PC, laptop, Smartphone and tablet either by normal network connection or by using 3G and 4G cellular network. The latter is important for the accessibility of the FLIRE DSS during firefighting or rescue operations during flood events. All these methods and tools provide the end users with the necessary information to design an operational plan for the elimination of the fire events and the efficient management of the flood events in almost real time. Concluding, the FLIRE DSS can be easily transferred to other areas with similar characteristics due to its robust architecture and its
Full Text Available Direct forecasting method for Urban Rail Transit (URT ridership at the station level is not able to reflect nonlinear relationship between ridership and its predictors. Also, population is inappropriately expressed in this method since it is not uniformly distributed by area. In this paper, a new variable, population per distance band, is considered and a back propagation neural network (BPNN model which can reflect nonlinear relationship between ridership and its predictors is proposed to forecast ridership. Key predictors are obtained through partial correlation analysis. The performance of the proposed model is compared with three other benchmark models, which are linear model with population per distance band, BPNN model with total population, and linear model with total population, using four measures of effectiveness (MOEs, maximum relative error (MRE, smallest relative error (SRE, average relative error (ARE, and mean square root of relative error (MSRRE. Also, another model for contribution rate of population per distance band to ridership is formulated based on the BPNN model with nonpopulation variables fixed. Case studies with Japanese data show that BPNN model with population per distance band outperforms other three models and the contribution rate of population within special distance band to ridership calculated through the contribution rate model is 70%~92.9% close to actual statistical value. The result confirms the effectiveness of models proposed in this paper.
Heinzlef, Charlotte; Ganz, François; Becue, Vincent; Serre, Damien
Since Hurricane Katrina (2005), the scientific-political-urban attention is focusing on urban resilience to floods. To prevent the recurrence of such a deadly and costly event ( 82 billion, Serre et al, 2014), experts began to question pre- and post- disaster management. Until now, managers and urban planners have been working on flood risk, according to the paradigm of prevention. However, following Katrina, a new approach was gradually integrated and the concept of resilience applied to urban areas (Serre, 2011). The resilience concept, used in ecology and defined by Holling (1973), refers to the ability of a system to keep its own variables despite changes and analyses the capacity of an (eco)system to tolerate disturbances without changing its state. To link it with flood risk management, this concept takes more into account water and would lead to technical, architectural, social, urban and political innovation (Serre et al, 2016). However, despite 12 years after Katrina, very few concrete actions have been made (Barroca and Serre, 2013). Based on this argument, and several abortive studies, we wish to re-address the operationalization of resilience by redefining its objectives and expectations. While in Europe some studies have been done to build up vulnerability indicators (Barroca et al, 2006; Opach et al, 2016; Wiréhn et al, 2016), few still talk about resilience. When some do (Folke et al, 2010; Lhomme et al, 2011; Nguyen et al, 2013; Suarez et al, 2016), they mainly speak about technical resilience without integrating social resilience. Our objective is thus to imagine a system facilitating the understanding of this concept, its integration in management and development policies. We started on the methodology of information systems, organized system for collection, organization, storage and communication of information, and more precisely on observatories, information systems using the methodology of observation. These last years, we assist to an
Magulova, Barbora; Caporali, Enrica; Bednarik, Martin
The case study presents the use of statistical methods and analysis tools, for hazard assessment of "urbanization units", implemented in a Geographic Information Systems (GIS) environment. As a case study, the Levoča region (Slovakia) is selected. The region, with a total area of about 351 km2, is widely affected by landslides and floods. The problem, for small urbanization areas, is nowadays particularly significant from the socio-economic point of view. It is considered, presently, also an increasing problem, mainly because of climate change and more frequent extreme rainfall events. The geo-hazards are evaluated using a multivariate analysis. The landslide hazard assessment is based on the comparison and subsequent statistical elaboration of territorial dependence among different input factors influencing the instability of the slopes. Particularly, five factors influencing slope stability are evaluated, i.e. lithology, slope aspect, slope angle, hypsographic level and present land use. As a result a new landslide susceptibility map is compiled and different zones of stable, dormant and non-stable areas are defined. For flood hazard map a detailed digital elevation model is created. A compose index of flood hazard is derived from topography, land cover and pedology related data. To estimate flood discharge, time series of stream flow and precipitation measurements are used. The assessment results are prognostic maps of landslide hazard and flood hazard, which presents the optimal base for urbanization planning.
Kaspersen, Per Skougaard; Høegh Ravn, N.; Arnbjerg-Nielsen, Karsten
In this paper we present a methodology suitable for investigating the relative and combined influence of urban land cover changes and climate change for the exposure of cities to pluvial flooding. A combined hydrological-hydrodynamic modelling and remote sensing approach enables the quantificatio...... during the past 30 years caused an increase in flood exposure that is comparable to what is expected in the RCP4.5 (+2°C) climate scenario.......In this paper we present a methodology suitable for investigating the relative and combined influence of urban land cover changes and climate change for the exposure of cities to pluvial flooding. A combined hydrological-hydrodynamic modelling and remote sensing approach enables the quantification...
Hettiarachchi, Suresh; Wasko, Conrad; Sharma, Ashish
The effects of climate change are causing more frequent extreme rainfall events and an increased risk of flooding in developed areas. Quantifying this increased risk is of critical importance for the protection of life and property as well as for infrastructure planning and design. The updated National Oceanic and Atmospheric Administration (NOAA) Atlas 14 intensity-duration-frequency (IDF) relationships and temporal patterns are widely used in hydrologic and hydraulic modeling for design and planning in the United States. Current literature shows that rising temperatures as a result of climate change will result in an intensification of rainfall. These impacts are not explicitly included in the NOAA temporal patterns, which can have consequences on the design and planning of adaptation and flood mitigation measures. In addition there is a lack of detailed hydraulic modeling when assessing climate change impacts on flooding. The study presented in this paper uses a comprehensive hydrologic and hydraulic model of a fully developed urban/suburban catchment to explore two primary questions related to climate change impacts on flood risk. (1) How do climate change effects on storm temporal patterns and rainfall volumes impact flooding in a developed complex watershed? (2) Is the storm temporal pattern as critical as the total volume of rainfall when evaluating urban flood risk? We use the NOAA Atlas 14 temporal patterns, along with the expected increase in temperature for the RCP8.5 scenario for 2081-2100, to project temporal patterns and rainfall volumes to reflect future climatic change. The model results show that different rainfall patterns cause variability in flood depths during a storm event. The changes in the projected temporal patterns alone increase the risk of flood magnitude up to 35 %, with the cumulative impacts of temperature rise on temporal patterns and the storm volume increasing flood risk from 10 to 170 %. The results also show that regional
Full Text Available The effects of climate change are causing more frequent extreme rainfall events and an increased risk of flooding in developed areas. Quantifying this increased risk is of critical importance for the protection of life and property as well as for infrastructure planning and design. The updated National Oceanic and Atmospheric Administration (NOAA Atlas 14 intensity–duration–frequency (IDF relationships and temporal patterns are widely used in hydrologic and hydraulic modeling for design and planning in the United States. Current literature shows that rising temperatures as a result of climate change will result in an intensification of rainfall. These impacts are not explicitly included in the NOAA temporal patterns, which can have consequences on the design and planning of adaptation and flood mitigation measures. In addition there is a lack of detailed hydraulic modeling when assessing climate change impacts on flooding. The study presented in this paper uses a comprehensive hydrologic and hydraulic model of a fully developed urban/suburban catchment to explore two primary questions related to climate change impacts on flood risk. (1 How do climate change effects on storm temporal patterns and rainfall volumes impact flooding in a developed complex watershed? (2 Is the storm temporal pattern as critical as the total volume of rainfall when evaluating urban flood risk? We use the NOAA Atlas 14 temporal patterns, along with the expected increase in temperature for the RCP8.5 scenario for 2081–2100, to project temporal patterns and rainfall volumes to reflect future climatic change. The model results show that different rainfall patterns cause variability in flood depths during a storm event. The changes in the projected temporal patterns alone increase the risk of flood magnitude up to 35 %, with the cumulative impacts of temperature rise on temporal patterns and the storm volume increasing flood risk from 10 to 170 %. The results
G. H. Schmitz
robust model parameter vectors with respect to one single objective. The consideration of multiple objectives is just possible by aggregation. In this paper, we present an approach that combines the principles of multi-objective optimisation and depth-based sampling, entitled Multi-Objective Robust Parameter Estimation (MOROPE. It applies a multi-objective optimisation algorithm in order to identify non-dominated robust model parameter vectors. Subsequently, it samples parameter vectors with high data depth using a further developed sampling algorithm presented in Krauße and Cullmann (2012a. We study the effectivity of the proposed method using synthetical test functions and for the calibration of a distributed hydrologic model with focus on flood events in a small, pre-alpine, and fast responding catchment in Switzerland.
Löwe, Roland; Thorndahl, Søren; Mikkelsen, Peter Steen
We investigate the application of rainfall observations and forecasts from rain gauges and weather radar as input to operational urban runoff forecasting models. We apply lumped rainfall runoff models implemented in a stochastic grey-box modelling framework. Different model structures are conside......We investigate the application of rainfall observations and forecasts from rain gauges and weather radar as input to operational urban runoff forecasting models. We apply lumped rainfall runoff models implemented in a stochastic grey-box modelling framework. Different model structures...
Wied Pedersen, Jonas; Lund, Nadia Schou Vorndran; Borup, Morten
High quality on-line flow forecasts are useful for real-time operation of urban drainage systems and wastewater treatment plants. This requires computationally efficient models, which are continuously updated with observed data to provide good initial conditions for the forecasts. This paper...... period of time that precedes the forecast. The method is illustrated for an urban catchment, where flow forecasts of 0–4 h are generated by applying a lumped linear reservoir model with three cascading reservoirs. Radar rainfall observations are used as input to the model. The effects of different prior...
Parry, Louise; Neely, Ryan, III; Bennett, Lindsay; Collier, Chris; Dufton, David
The Scottish Environment Protection Agency (SEPA) has a statutory responsibility to provide flood warning across Scotland. It achieves this through an operational partnership with the UK Met Office wherein meteorological forecasts are applied to a national distributed hydrological model, Grid- to- Grid (G2G), and catchment specific lumped PDM models. Both of these model types rely on observed precipitation input for model development and calibration, and operationally for historical runs to generate initial conditions. Scotland has an average annual precipitation of 1430mm per annum (1971-2000), but the spatial variability in totals is high, predominantly in relation to the topography and prevailing winds, which poses different challenges to both radar and point measurement methods of observation. In addition, the high elevations mean that in winter a significant proportion of precipitation falls as snow. For the operational forecasting models, observed rainfall data is provided in Near Real Time (NRT) from SEPA's network of approximately 260 telemetered TBR gauges and 4 UK Met Office C-band radars. Both data sources have their strengths and weaknesses, particularly in relation to the orography and spatial representativeness, but estimates of rainfall from the two methods can vary greatly. Northern Scotland, particularly near Inverness, is a comparatively sparse part of the radar network. Rainfall totals and distribution in this area are determined by the Northern Western Highlands and Cairngorms mountain ranges, which also have a negative impact on radar observations. In recognition of this issue, the NCAS mobile X-band weather radar (MXWR) was deployed in this area between February and August 2016. This study presents a comparison of rainfall estimates for the Inverness and Moray Firth region generated from the operational radar network, the TBR network, and the MXWR. Quantitative precipitation estimates (QPEs) from both sources of radar data were compared to
Arrighi, Chiara; Campo, Lorenzo
In last years, the concern about the economical and lives loss due to urban floods has grown hand in hand with the numerical skills in simulating such events. The large amount of computational power needed in order to address the problem (simulating a flood in a complex terrain such as a medium-large city) is only one of the issues. Among them it is possible to consider the general lack of exhaustive observations during the event (exact extension, dynamic, water level reached in different parts of the involved area), needed for calibration and validation of the model, the need of considering the sewers effects, and the availability of a correct and precise description of the geometry of the problem. In large cities the topographic surveys are in general available with a number of points, but a complete hydraulic simulation needs a detailed description of the terrain on the whole computational domain. LIDAR surveys can achieve this goal, providing a comprehensive description of the terrain, although they often lack precision. In this work an optimal merging of these two sources of geometrical information, measured elevation points and LIDAR survey, is proposed, by taking into account the error variance of both. The procedure is applied to a flood-prone city over an area of 35 square km approximately starting with a DTM from LIDAR with a spatial resolution of 1 m, and 13000 measured points. The spatial pattern of the error (LIDAR vs points) is analysed, and the merging method is tested with a series of Jackknife procedures that take into account different densities of the available points. A discussion of the results is provided.
Leitão, J P; Boonya-Aroonnet, S; Prodanović, D; Maksimović, C
This paper presents the developments towards the next generation of overland flow modelling of urban pluvial flooding. Using a detailed analysis of the Digital Elevation Model (DEM) the developed GIS tools can automatically generate surface drainage networks which consist of temporary ponds (floodable areas) and flow paths and link them with the underground network through inlets. For different commercially-available Rainfall-Runoff simulation models, the tool will generate the overland flow network needed to model the surface runoff and pluvial flooding accurately. In this paper the emphasis is placed on a sensitivity analysis of ponds and preferential overland flow paths creation. Different DEMs for three areas were considered in order to compare the results obtained. The DEMs considered were generated using different acquisition techniques and hence represent terrain with varying levels of resolution and accuracy. The results show that DEMs can be used to generate surface flow networks reliably. As expected, the quality of the surface network generated is highly dependent on the quality and resolution of the DEMs and successful representation of buildings and streets.
Hu, Yijia; Zhong, Zhong; Zhu, Yimin; Ha, Yao
In this paper, a statistical forecast model using the time-scale decomposition method is established to do the seasonal prediction of the rainfall during flood period (FPR) over the middle and lower reaches of the Yangtze River Valley (MLYRV). This method decomposites the rainfall over the MLYRV into three time-scale components, namely, the interannual component with the period less than 8 years, the interdecadal component with the period from 8 to 30 years, and the interdecadal component with the period larger than 30 years. Then, the predictors are selected for the three time-scale components of FPR through the correlation analysis. At last, a statistical forecast model is established using the multiple linear regression technique to predict the three time-scale components of the FPR, respectively. The results show that this forecast model can capture the interannual and interdecadal variation of FPR. The hindcast of FPR during 14 years from 2001 to 2014 shows that the FPR can be predicted successfully in 11 out of the 14 years. This forecast model performs better than the model using traditional scheme without time-scale decomposition. Therefore, the statistical forecast model using the time-scale decomposition technique has good skills and application value in the operational prediction of FPR over the MLYRV.
ten Veldhuis, Marie-claire; Zhou, Zhengzheng; Yang, Long; Liu, Shuguang; Smith, James
The impact of spatial and temporal variability of rainfall on hydrological response remains poorly understood, in particular in urban catchments due to their strong variability in land use, a high degree of imperviousness and the presence of stormwater infrastructure. In this study, we analyze the effect of storm scale, position and movement in relation to basin scale and flow-path network structure on urban hydrological response. A catalog of 279 peak events was extracted from a high-quality observational dataset covering 15 years of flow observations and radar rainfall data for five (semi)urbanized basins ranging from 7.0 to 111.1 km2 in size. Results showed that the largest peak flows in the event catalog were associated with storm core scales exceeding basin scale, for all except the largest basin. Spatial scale of flood-producing storm events in the smaller basins fell into two groups: storms of large spatial scales exceeding basin size or small, concentrated events, with storm core much smaller than basin size. For the majority of events, spatial rainfall variability was strongly smoothed by the flow-path network, increasingly so for larger basin size. Correlation analysis showed that position of the storm in relation to the flow-path network was significantly correlated with peak flow in the smallest and in the two more urbanized basins. Analysis of storm movement relative to the flow-path network showed that direction of storm movement, upstream or downstream relative to the flow-path network, had little influence on hydrological response. Slow-moving storms tend to be associated with higher peak flows and longer lag times. Unexpectedly, position of the storm relative to impervious cover within the basins had little effect on flow peaks. These findings show the importance of observation-based analysis in validating and improving our understanding of interactions between the spatial distribution of rainfall and catchment variability.
Aroca-Jimenez, Estefanía; Bodoque, Jose Maria; García, Juan Antonio; Diez-Herrero, Andres
The growth of exposed population to floods, the expansion in allocation of economical activities to flood-prone areas and the rise of extraordinary event frequency over the last few decades, have resulted in an increase of flash flood-related casualties and economic losses. The increase in these losses at an even higher rate than the increase of magnitude and frequency of extreme events, underline that the vulnerability of societies exposed is a key aspect to be considered. Vulnerability is defined as the conditions determined by physical, social, economic and environmental factors or processes which increase the susceptibility of a community to the impact of hazards such as floods, being flash floods one of the natural hazards with the greatest capacity to generate risk. In recent years, numerous papers have deal with the assessment of the social dimension of vulnerability. However, economic factors are often a neglected aspect in traditional risk assessments which mainly focus on structural measures and flood damage models. In this context, the aim of this research is to identify those economic characteristics which render people vulnerable to flash flood hazard, and consider whether these characteristics are identifiable as local patterns at regional level. The result of this task is an Economic Vulnerability Index (EVI) based on susceptibility profiles of the population per township. These profiles are obtained by Hierarchical Segmentation and Latent Class Cluster Analysis of economic information provided by different public institutional databases. The methodology proposed here is implemented in the region of Castilla y León (94,230 km2), placed in Central-Northern Spain. Townships included in this study meet two requirements: i) urban areas are potentially affected by flash floods (i.e. villages are crossed by rivers or streams with a longitudinal slope higher than 0.01 m m-1); ii) urban areas are affected by an area with low or exceptional probability of
Floods are natural phenomena, but damage and losses from floods are the consequence of human action. The increasing climatic variability, storminess and more frequent flooding driven by climate change will affect poor urban communities far more than other people living in towns and cities. Although driven by human activities ranging from modernisation and development to land degradation by poor farmers and grazing flocks, climate change in Africa has uneven impacts, affecting the poor severely. Flooding in urban areas is not just related to heavy rainfall and extreme climatic events; it is also related to changes in the built-up areas themselves. Urbanisation aggravates flooding by restricting where floods waters can go, by covering large parts of the ground with roofs, roads and pavements, by obstructing sections of natural channels, and by building drains that ensure that water moves to rivers more rapidly than it did under natural conditions. As people crowd into African cities, these human impacts on urban land surfaces and drainage intensify. The proportions of small stream and river catchment areas that are urbanised will increase. As a result, even quite moderate storms now produce quite high flows in rivers because much more of the catchment area supplies direct surface runoff from its hard surfaces and drains. Where streams flow through a series of culverts and concrete channels, they cannot adjust to changes in the frequency of heavy rain as natural streams do. They often get obstructed by silt and urban debris, particularly when houses are built close to the channels. Such situations frequently arise where poor people build their shelters on low-lying flood plains, over swamps or above the tidewater on the coast. The effects of climate change are superimposed on these people-driven local land surface modifications. The links between changes in land use and in heavy rainfall patterns, the frequency and depth of flooding and the problems of the urban poor
Dalu, Mwazvita T. B.; Shackleton, Charlie M.
Rapid and widespread land cover change and the subsequent loss of the buffering capacity provided by healthy ecosystems against natural hazards has resulted in increased vulnerability to natural hazards. There is an insufficient understanding of the natural resources contribution to the resilience of poor urban communities living in informal settlements and the financial implications thereof. Thus, household strategies used to recover from the October 2012 flood shock were investigated within the informal settlements of three small South African towns using questionnaires. Within the vulnerability paradigm and the sustainable livelihood framework, the study also quantified and evaluated the relative contribution of natural resources to recovery strategies and the impacts on household financial capital. We found that natural resources contributed up to 70% to recovery of households from the flood shock, most of this being to reconstruct housing structures after the flood. Factors such as household head education level, household income, kinship level, the extent of property damage and the cost associated with property rehabilitation significantly influenced the uptake of natural resources in recovery from floods, and this was variable among settlements and towns. The main findings showed that natural resources reduced household vulnerability of urban informal settlements by providing an emergency-net function that substitutes financial capital. Their inclusion in disaster management plans and responses has the potential to contribute to the sustainable livelihoods of the urban poor in the Eastern Cape, South Africa.
Gires, A.; Giangola-Murzyn, A.; Tchiguirinskaia, I.; Schertzer, D.; Lovejoy, S.
During a flood in urban area, several non-linear processes (rainfall, surface runoff, sewer flow, and sub-surface flow) interact. Fully distributed hydrological models are a useful tool to better understand these complex interactions between natural processes and man built environment. Developing an efficient model is a first step to improve the understanding of flood resilience in urban area. Given that the previously mentioned underlying physical phenomenon exhibit different relevant scales, determining the required spatial resolution of such model is tricky but necessary issue. For instance such model should be able to properly represent large scale effects of local scale flood resilience measures such as stop logs. The model should also be as simple as possible without being simplistic. In this paper we test two types of model. First we use an operational semi-distributed model over a 3400 ha peri-urban area located in Seine-Saint-Denis (North-East of Paris). In this model, the area is divided into sub-catchments of average size 17 ha that are considered as homogenous, and only the sewer discharge is modelled. The rainfall data, whose resolution is 1 km is space and 5 min in time, comes from the C-band radar of Trappes, located in the West of Paris, and operated by Météo-France. It was shown that the spatial resolution of both the model and the rainfall field did not enable to fully grasp the small scale rainfall variability. To achieve this, first an ensemble of realistic rainfall fields downscaled to a resolution of 100 m is generated with the help of multifractal space-time cascades whose characteristic exponents are estimated on the available radar data. Second the corresponding ensemble of sewer hydrographs is simulated by inputting each rainfall realization to the model. It appears that the probability distribution of the simulated peak flow exhibits a power-law behaviour. This indicates that there is a great uncertainty associated with small scale
Full Text Available Flood maps alone are not sufficient to determine and assess the risks to people, property, infrastructure, and services due to a flood event. Simply put, the risk is almost zero to minimum if the flooded region is “empty” (i.e., unpopulated, has not properties, no industry, no infrastructure, and no socio-economic activity. High spatial resolution Earth Observation (EO data can contribute to the generation and updating of flood risk maps based on several aspects including population, economic development, and critical infrastructure, which can enhance a city’s flood mitigation and preparedness planning. In this case study for the Don River watershed, Toronto, the flood risk is determined and flood risk index maps are generated by implementing a methodology for estimating risk based on the geographic coverage of the flood hazard, vulnerability of people, and the exposure of large building structures to flood water. Specifically, the spatial flood risk index maps have been generated through analytical spatial modeling which takes into account the areas in which a flood hazard is expected to occur, the terrain’s morphological characteristics, socio-economic parameters based on demographic data, and the density of large building complexes. Generated flood risk maps are verified through visual inspection with 3D city flood maps. Findings illustrate that areas of higher flood risk coincide with areas of high flood hazard and social and building exposure vulnerability.
M.-C. ten Veldhuis
Full Text Available The impact of spatial and temporal variability of rainfall on hydrological response remains poorly understood, in particular in urban catchments due to their strong variability in land use, a high degree of imperviousness and the presence of stormwater infrastructure. In this study, we analyze the effect of storm scale, position and movement in relation to basin scale and flow-path network structure on urban hydrological response. A catalog of 279 peak events was extracted from a high-quality observational dataset covering 15 years of flow observations and radar rainfall data for five (semiurbanized basins ranging from 7.0 to 111.1