Flood Prediction Model Research Articles

A Microscopic Simulation of Evacuation Model Considering Car-Following Behavior under Flood

This study developed a microscopic simulation evacuation model considering car-following behavior in flood situation using software AIMSUN. The authors formulated the relationship between car-following free velocity and depth of water by information with respect to the accurate representation of vehicle's dynamics properties under flood. Moreover, a flood prediction model combining traffic analysis is conducted to catch up with the momentarily changed network because of the disaster. The effectiveness of different departure models of human behavior are also compared. They are exponential and simultaneous activities. The results indicate that the flood significantly affects the evacuation and that strategies promoting smooth evacuation activity are very important.

Signature-based model calibration for hydrological prediction in mesoscale Alpine catchments

This paper presents a calibration framework for a precipitation–runoff model for flood prediction in a mesoscale Alpine basin with discharges strongly influenced by hydraulic works. The developed methodology addresses two classical hydrological calibration challenges: computational limitations to run optimization algorithms for distributed hourly models and the absence of concomitant meteorological and natural discharge time series. The presented processes-oriented, multi-signal approach is based on hydrological data from a variety of sources and for different periods, corresponding to various spatial scales. The model parameters are calibrated by sequentially minimizing differences between observed and simulated values for different hydrological signals and signatures such as: (a) the phase of precipitations, (b) the time evolution of point-scale snow heights, (c) the mean inter-annual cycle of daily discharges, and (d) timing of snowmelt-induced spring runoff. We compare the model performance to a benchmark model obtained by simply using the globally optimal parameter values from the nearest gauged and non perturbed catchment. For prediction of flow seasonality and also extreme events, the calibration methodology outperforms the benchmark. Citation Hingray, B., Schaefli, B., Mezghani, A. & Hamdi, Y. (2010) Signature-based model calibration for hydrological prediction in mesoscale Alpine catchments. Hydrol. Sci. J. 55(6), 1002–1016.

A digitized global flood inventory (1998–2008): compilation and preliminary results

Floods have profound impacts on populations worldwide in terms of both loss of life and property. A global inventory of floods is an important tool for quantifying the spatial and temporal distribution of floods and for evaluating global flood prediction models. Several global hazard inventories currently exist; however, their utility for spa- tiotemporal analysis of global floods is limited. The existing flood catalogs either fail to record the geospatial area over which the flood impacted or restrict the types of flood events included in the database according to a set of criteria, limiting the scope of the inventory. To improve upon existing databases, and make it more comprehensive, we have compiled a digitized Global Flood Inventory (GFI) for the period 1998-2008 which also geo-references each flood event by latitude and longitude. This technical report presents the methodology used to compile the GFI and preliminary findings on the spatial and temporal distributions of the flooding events that are contained in the inventory.

Evaluation of the real-time TRMM-based multi-satellite precipitation analysis for an operational flood prediction system in Nzoia Basin, Lake Victoria, Africa

Many researchers seek to take advantage of the recently available and virtually uninterrupted supply of satellite-based rainfall information as an alternative and supple- ment to the ground-based observations in order to implement a cost-effective flood prediction in many under-gauged regions around the world. Recently, NASA Applied Science Program has partnered with USAID and African-RCMRD to implement an operational water-hazard warning system, SERVIR-Africa. The ultimate goal of the project is to build up disaster management capacity in East Africa by providing local governmental officials and international aid organizations a practical decision-support tool in order to better assess emerging flood impacts and to quantify spatial extent of flood risk, as well as to respond to such flood emergencies more expediently. The objective of this article is to evaluate the applicability of integrating NASA's standard satellite precipitation product with a flood prediction model for disaster management in Nzoia, sub-basin of Lake Vic- toria, Africa. This research first evaluated the TMPA real-time rainfall data against gauged rainfall data from the year 2002 through 2006. Then, the gridded Xinanjiang Model was calibrated to Nzoia basin for period of 1985-2006. Benchmark streamflow simulations were produced with the calibrated hydrological model using the rain gauge and observed

A Microcomputer Model for Flood Prediction with Applications

: A generally applicable mathematical model, tailored for use on a Personal Computer, as the design tool to simulate unsteady flood flow in a network of interconnected rivers has been developed and applied to the Sham Chun River along the border between Hong Kong and the People's Republic of China. The effect on the improved Sham Chun River due to the proposed river training in the River Indus, one of its tributaries, is evaluated. The one-dimensional unsteady flow model itself is broadly applicable to a wide range of hydrologic conditions and varying field situations. It incorporates real hydraulic features including branched channels and ‘tidal flats’ flooding and drying. The time history of water stages and discharges are computed at any desired location throughout the connected essentially one-dimensional channel network subject to initial and boundary conditions. Details are given of techniques adapted in using the computational scheme to quantify the unsteady flow condition and the means to assure an efficient numerical solution as well as to verify the accuracy of the computed outputs by subjection to carefully selected schematic simplified test cases and real prototype cases with numerical solution method is a useful predictive engineering tool in river design.

Applicability of a Distributed Hydrological Model for Flood prediction in Different Climatic Regions

Applicability of a Distributed Hydrological Model for Flood prediction in Different Climatic Regions

Uncertainty analysis of flash‐flood system using remote sensing and a geographic information system based on GcIUH in the Yeongdeok Basin, Korea

The current paper focuses on minimizing flood damage in the Yeongdeok basin of South Korea by establishing a flood prediction model based on a geographic information system (GIS), remote sensing and geomorphoclimatic instantaneous unit hydrograph (GcIUH) techniques. The GIS database for flash flood prediction was created using data from digital elevation models (DEMs), soil maps and Landsat satellite imagery. Flood prediction was based on the peak discharge calculated at the sub‐basin scale using hydrogeomorphologic techniques and the threshold runoff value. Using the developed flash flood prediction model, rainfall conditions with the potential to cause flooding were determined based on the cumulative rainfall for 20 min, considering rainfall duration, peak discharge and flooding in the Yeongdeok basin.

Integrated assessment of changes in flooding probabilities due to climate change

An approach to considering changes in flooding probability in the integrated assessment of climate change is introduced. A reduced-form hydrological model for flood prediction and a downscaling approach suitable for integrated assessment modeling are presented. Based on these components, the fraction of world population living in river basins affected by changes in flooding probability in the course of climate change is determined. This is then used as a climate impact response function in order to derive emission corridors limiting the population affected. This approach illustrates the consideration of probabilistic impacts within the framework of the tolerable windows approach. Based on the change in global mean temperature, as calculated by the simple climate models used in integrated assessment, spatially resolved changes in climatic variables are determined using pattern scaling, while natural variability in these variables is considered using twentieth century deviations from the climatology. Driven by the spatially resolved climate change, the hydrological model then aggregates these changes to river basin scale. The hydrological model is subjected to a sensitivity analysis with regard to the water balance, and the uncertainty arising through the different projections of changes in mean climate by differing climate models is considered by presenting results based on different models. The results suggest that up to 20% of world population live in river basins that might inevitably be affected by increased flood events in the course of global warming, depending on the climate model used to estimate the regional distribution of changes in climate.

Verification of a coupled hydrometeorological modelling approach for alpine tributaries in the Rhine basin

This study describes the results of a model setup for the off-line one-way coupling of a numerical weather prediction (NWP) model with the hydrological PREVAH model for flood prediction and its multi-year validation. The hydrological model has been applied to the Rhine basin down to the gauge Rheinfelden (34,550 km 2) with a spatial resolution of 500 by 500 m 2 using an hourly time-step. The calibration of the hydrological model was based on surface observations and has been carried out for each of the 23 subcatchments, whose runoff regimes range from glacial to nival and pluvial. Because of the presence of lakes in the investigated catchments, it was a necessity to include a function which represents the retarding and flattening of flood peaks by lakes. Results show that the model is capable to reproduce the relevant hydrological processes in the investigated catchments and that the model properly captures the extreme runoff peaks both during the calibration (1997–1998) and validation period (1999–2002).Validation runs with 19 to 42 h atmospheric forecasts for the period 1997–2002 show, as expected, a decrease in model accuracy. The use of precipitation forecasts considerably increases the False Alarm Rate (FAR), while the Critical Success Index (CSI) decreases. Consequences of errors in the precipitation forecasts are most pronounced for higher thresholds, while the coupled modelling system performs better for smaller precipitation events. The effects of errors in the temperature forecasts are most pronounced in spring and summer. This is caused by an overestimation of snowpack accumulation in winter, which consequently results in too much snowmelt during the melt season in the upper catchments.

Radar hydrology: rainfall estimation

Radar observations of rainfall and their use in hydrologic research provide the focus for the paper. Radar-rainfall products are crucial for input to runoff and flood prediction models, validation of satellite remote sensing algorithms, and for statistical characterization of extreme rainfall frequency. In this context we discuss the issues of radar-rainfall product development, and the theoretical and practical requirements of validating radar-rainfall maps and new radar technologies. We discuss a framework for reflectivity based rainfall estimation, including estimation of uncertainty of radar-rainfall estimates. Validation of radar-rainfall products is a major challenge for broad utilization of these products in hydrologic applications. In the discussion of radar-rainfall prediction we focus on orographically induced extreme rainfall and flooding, discuss the issues of detection, statistical sample size, and scale effects. We conclude the paper with a set of recommendations for research priorities and experimental requirements to address them.

A real-time hydrological model for flood prediction using GIS and the WWW

The purpose of this paper is to examine the current status of real time hydrological models used for flood nowcasting and hazard mitigation and indicate how WWW-based systems can overcome some of the limitations of existing systems. Whilst hydrologically innovative and robust models are available, they are poorly suited to real time application, are often not well integrated with spatial datasets such as GIS. Current systems also lack flexibility, customisability and accessibility by a range of end users. We describe the development of a Web-based hydrological modelling system that permits integrated handling of real-time rainfall data from a wireless monitoring network. A spatially distributed GIS-based model is integrated on the basis of this incoming data, approximating real-time to produce data on catchment hydrology and runoff. The data can be accessed from any WWW interface, and they can be analysed online using a number of GIS and numerical functions. We discuss the potential users of such a system and the requirements for interfacing model output with these users for hydrological nowcasting and spatial real-time, emergency decision support. Rather than discuss developments in the modelling of hydrology for flood hazard mitigation, this paper focuses on developments in interfacing these models with end users.

On the improvement of real-time flood prediction model for dam operation

In order to improve the flood forcasting accuracy for dam operation, we developed a real-time prediction system. The system is based on the filtering and prediction theory developed by Kalman and others. A lumped runoff model is transformed into a stochastic model by incorporating noise terms. The system takes account of the fact that the optimum values of the model parameters change from flood to flood. Three kinds of parameter are selected through the sensitivity analysis for many runoff data. Discharges are forcasted by use of prediction systems more than one, with different values of parameter respectively. Hence contributions to runoff is evaluated for each predicted discharge stochastically. Then most suitable discharge for flood runoff specified is determined by a weighting average method.

The real time runoff forecast models for the River Dijle

The paper deals with a real time correction model for application in combination with a conceptual real time rainfall-runoff model for flood prediction. Autoregressive moving average time series models are used for this purpose. For the presented case study, a third-order autoregressive model has been calibrated. The calibration procedure is briefly discussed and the results of quality tests are elaborated. The possibilities and limitations of the correction procedure are illustrated for a test basin.

Expert System for Flood-Control Operation of Reservoir

An expert system for the real-time operation of a flood-control reservoir is formulated and its applicability to the extreme floods is examined. We show that the ordinary rules of operation of the reservoir can be described by production rules. When the conventional rules of operation are used during the flood where inflow exceeds that of the design flood, the outflow increases rapidly and has a large peak. Using the inference system which includes a flood prediction model and optimizing routines, these problems can be resolved.

Mathematical model for flood and embankment prediction in a tidal river

A mathematical model is developed for a study of the interaction of an incoming tide from the downstream end with an abnormal flood discharge from landward end in a tidal river. It is shown that the study determines the maximum flood level and in turn the design height and spacing of embankments in the river. The problem is solved numerically by using the explicite finite difference scheme combined with the leap-frog operator. The computational results of the model are compared with the observed data in the river Rupnarain.