Short‐term flood inundation prediction using hydrologic‐hydraulic models forced with downscaled rainfall from global NWP

A short‐term flood inundation prediction model has been formulated based on the combination of the super‐tank model, forced with downscaled rainfall from a global numerical weather prediction model, and a one‐dimensional (1D) hydraulic model. Different statistical methods for downscaled rainfall have been explored, taking into account the availability of historical data. It has been found that the full implementation of a statistical downscaling model considering physically‐based corrections to the numerical weather prediction model output for rainfall prediction performs better compared with an altitudinal correction method. The integration of the super‐tank model into the 1D hydraulic model demonstrates a minimal requirement for the calibration of rainfall–runoff and flood propagation models. Updating the model with antecedent rainfall and regular forecast renewal has enhanced the model's capabilities as a result of the data assimilation processes of the runoff and numerical weather prediction models. The results show that the predicted water levels demonstrate acceptable agreement with those measured by stream gauges and comparable to those reproduced using the actual rainfall. Moreover, the predicted flood inundation depth and extent exhibit reasonably similar tendencies to those observed in the field. However, large uncertainties are observed in the prediction results in lower, flat portions of the river basin where the hydraulic conditions are not properly analysed by the 1D flood propagation model. Copyright © 2013 John Wiley & Sons, Ltd.     

A review of low‐cost space‐borne data for flood modelling: topography,flood extent and water level

During the last two decades, remote sensing data have led to tremendous progress in advancing flood inundation modelling. In particular, low‐cost space‐borne data can be invaluable for large‐scale flood studies in data‐scarce areas. Various satellite products yield valuable information such as land surface elevation, flood extent and water level, which could potentially contribute to various flood studies. An increasing number of research studies have been dedicated to exploring those low‐cost data towards building, calibration and evaluation, and remote‐sensed information assimilation into hydraulic models. This paper aims at reviewing these recent scientific efforts on the integration of low‐cost space‐borne remote sensing data with flood modelling. Potentials and limitations of those data in flood modelling are discussed. This paper also introduces the future satellite missions and anticipates their likely impacts in flood modelling. Copyright © 2015 John Wiley & Sons, Ltd.     

Predicting floodplain inundation: raster‐based modelling versus the finite‐element approach

We compare two approaches to modelling floodplain inundation: a raster‐based approach, which uses a relatively simple process representation, with channel flows being resolved separately from the floodplain using either a kinematic or diffusive wave approximation, and a finite‐element hydraulic model aiming to solve the full two‐dimensional shallow‐water equations. A flood event on a short (c. 4 km) reach of the upper River Thames in the UK is simulated, the models being validated against inundation extent as determined from satellite synthetic aperture radar (SAR) imagery. The unconstrained friction parameters are found through a calibration procedure, where a measure of fit between predicted and observed shorelines is maximized. The raster and finite‐element models offer similar levels of performance, both classifying approximately 84% of the model domain correctly, compared with 65% for a simple planar prediction of water surface elevation. Further discrimination between models is not possible given the errors in the validation data. The simple raster‐based model is shown to have considerable advantages in terms of producing a straightforward calibration process, and being robust with respect to channel specification. Copyright © 2001 John Wiley & Sons, Ltd.     

Integrating remote sensing observations of flood hydrology and hydraulic modelling

The further development of two-dimensional finite element models of river flood flow is currently constrained by a lack of data for rigorous parameterization and validation. Remote sensing techniques have the potential to overcome a number of these constraints thereby allowing a research design for model development. This is illustrated with reference to a case study of a two-dimensional finite element model applied to the Missouri River, Nebraska and compared with a synchronous Landsat TM image of flood inundation extent. The case study allows research needs for the integration of hydraulic modelling and remote sensing to be defined. © 1997 John Wiley & Sons, Ltd.     

An integrated approach of flash flood analysis in ungauged Mediterranean watersheds using post-flood surveys and unmanned aerial vehicles

This study analyzes the flash flood event of two ungauged ephemeral streams in Olympiada region (Chalkidiki, North Greece), which occurred at the 21–22 of November 2019. Aim of the study is to reconstruct the specific flash flood event, investigate the causes of flood generation mechanisms, evaluate the performance of SCS-CN hydrological and HEC-RAS hydraulic models, investigate the relation between extreme flash floods and human intervention, using the combination of ground and aerial observations obtained from the field survey and unmanned aerial vehicles (UAVs), respectively. The results of the specific discharge ranged between 9 and 11 m³ s⁻¹ km², values that are typical for flash flood events in Mediterranean region. The comparison between the observed and simulated values of flood extent showed sufficiently good performance of the hydraulic model (CSI = 82%). However, the statistical analysis of the observed and simulated flood depths displayed a flood depth overestimation by the applied model, despite that the values of the used statistic indexes are acceptable (RMSE = 0.35 m, SD = 0.53, NSE = 0.56, PBIAS = 11.26%). The model overestimation of flood depth was attributed to the DEM low resolution and quality. Ground and aerial observations depicted the alluvial fan activation, the alternation of flow paths and the huge sediment transport. Human intervention in main streams, urban sprawl, wet AMC and sediment transport were among the main factors that contributed to the flash flood generation. This integrated approach revealed the necessity of the constant evaluation and validation of hydrological and hydraulic models in small ungauged Mediterranean watersheds and ephemeral streams. The use of UAVs in combination with ground observations and hydraulic simulation could significantly contribute to the enhanced understanding of flash flood mechanisms, in the direction of flood risk mitigation, improvement of the planning efficiency of flood prevent measures, flood hazard estimation, evolution of flood warning systems and floodplain geomorphology analysis.     

Investigating the influence of minor hydraulic structures on modeling flood events in lowland areas

Flood inundation models have been recognized to be a valuable tool to reproduce flow dynamics in a given area and support decision‐making processes on flood management measures. In many cases, in the simulation of flood events, only the main river channel and the associated structures are represented within the model. However, during flood events involving lowland areas, the minor drainage network – and the associated hydraulic structures – may have an important role in conveying flow and determining which areas will be flooded. The objective of this study is to investigate whether – and to what extent – small hydraulic structures in drainage networks have an influence in flooding on lowland areas. The case study for this research is the 1990 flood event which occurred in the lowland plain of the Reno River, in Northern Italy. The study area is mainly used for agricultural purposes and has a drainage system with several small bridges and culverts. The influence of the minor hydraulic structures on flood dynamics was analyzed through a combined use of one‐dimensional (1D) and two‐dimensional (2D) hydraulic models. First, a number of detailed and simplified approaches to represent hydraulic structures in the computational grids were analyzed by means of the HECRAS 1D model. Second, these approaches were implemented and tested in several 2D simulations of the flood event. The simulated inundation extents and flood levels were then compared with the observed data and with each other. The analysis of results showed that simplified schematizations were sufficient to obtain good model predictions of peak inundation extent and flood levels, at least for the present case study. Moreover, the influence of the structures on the peak flood inundation extent and flood levels was found to be limited, whereas it showed to be more significant during the drainage phase of the flood. Copyright © 2013 John Wiley & Sons, Ltd.     

Performance of a conceptual and physically based model in simulating the response of a semi‐urbanized watershed in San Antonio,Texas

The need for accurate hydrologic analysis and rainfall–runoff modelling tools has been rapidly increasing because of the growing complexity of operational hydrologic and hydraulic problems associated with population growth, rapid urbanization and expansion of agricultural activities. Given the recent advances in remote sensing of physiographic features and the availability of near real‐time precipitation products, rainfall–runoff models are expected to predict runoff more accurately. In this study, we compare the performance and implementation requirements of two rainfall–runoff models for a semi‐urbanized watershed. One is a semi‐distributed conceptual model, the Hydrologic Engineering Center‐Hydrologic Modelling System (HEC‐HMS). The other is a physically based, distributed‐parameter hydrologic model, the Gridded Surface Subsurface Hydrologic Analysis (GSSHA). Four flood events that took place on the Leon Creek watershed, a sub‐watershed of the San Antonio River basin in Texas, were used in this study. The two models were driven by the Multisensor Precipitation Estimator radar products. One event (in 2007) was used for HEC‐HMS and GSSHA calibrations. Two events (in 2004 and 2007) were used for further calibration of HEC‐HMS. Three events (in 2002, 2004 and 2010) were used for model validation. In general, the physically based, distributed‐parameter model performed better than the conceptual model and required less calibration. The two models were prepared with the same minimum required input data, and the effort required to build the two models did not differ substantially. Copyright © 2012 John Wiley & Sons, Ltd.     

ROC‐based calibration of flood inundation models

The use of spatial patterns of flood inundation (often obtained from remotely sensed imagery) to calibrate flood inundation models has been widespread over the last 15 years. Model calibration is most often achieved by employing one or even several performance measures derived from the well‐known confusion matrix based on a binary classification of flooding. However, relatively early on, it has been recognized that the use of commonly reported performance measures for calibrating flood inundation models (such as the F measure) is hampered because the calibration procedure commonly utilizes only one possible solution of a wet/dry classification of a remote sensing image [most often acquired by a synthetic aperture radar (SAR)] to calibrate or validate models and are biased towards either over‐prediction or under‐prediction of flooding. Despite the call in several studies for an alternative statistic, to this date, very few, if any, unbiased performance measure based on the confusion matrix has been proposed for flood model calibration/validation studies. In this paper, we employ a robust statistical measure that operates in the receiver operating characteristics (ROC) space and allows automated model calibration with high identifiability of the best model parameter set but without the need of a classification of the SAR image. The ROC‐based method for flood model calibration is demonstrated using two different flood event test cases with flood models of varying degree of complexity and boundary conditions with varying degree of accuracy. Verification of the calibration results and optional SAR classification is successfully performed with independent observations of the events. We believe that this proposed alternative approach to flood model calibration using spatial patterns of flood inundation should be employed instead of performance measures commonly used in conjunction with a binary flood map. © 2013 California Institute of Technology. Hydrological Processes © 2013 John Wiley & Sons, Ltd.     

Problems with binary pattern measures for flood model evaluation

As the calibration and evaluation of flood inundation models are a prerequisite for their successful application, there is a clear need to ensure that the performance measures that quantify how well models match the available observations are fit for purpose. This paper evaluates the binary pattern performance measures that are frequently used to compare flood inundation models with observations of flood extent. This evaluation considers whether these measures are able to calibrate and evaluate model predictions in a credible and consistent way, i.e. identifying the underlying model behaviour for a number of different purposes such as comparing models of floods of different magnitudes or on different catchments. Through theoretical examples, it is shown that the binary pattern measures are not consistent for floods of different sizes, such that for the same vertical error in water level, a model of a flood of large magnitude appears to perform better than a model of a smaller magnitude flood. Further, the commonly used Critical Success Index (usually referred to as F^{<2 >}) is biased in favour of overprediction of the flood extent, and is also biased towards correctly predicting areas of the domain with smaller topographic gradients. Consequently, it is recommended that future studies consider carefully the implications of reporting conclusions using these performance measures. Additionally, future research should consider whether a more robust and consistent analysis could be achieved by using elevation comparison methods instead. Copyright © 2013 John Wiley & Sons, Ltd.     

Real-time correction of water stage forecast during rainstorm events using combination of forecast errors

This study proposes a real-time error correction method for the forecasted water stage using a combination of forecast errors estimated by the time series models, AR(1), AR(2), MA(1) and MA(2), and the average deviation model to update the water stage forecast during rainstorm events. During flood forecasting and warning operations, the proposed real-time error correction method takes advantage of being individually and continuously implemented and the results not being updated to the hydrological model and hydraulic routings so as to save computational time by recalibrating the parameters of the proposed methods with real-time observation. For model validation, the current study adopts the observed and forecasted data on a severe typhoon, Morakot, collected at eight water level gauges in Southern Taiwan and provided by the flood forecast system FEWS_Taiwan, which is linked with the reliable quantitative precipitation forecast (QPF) at 3 h of lead time provided by the Center Weather Bureau in Taiwan, as the model validation. The results of numerical experiments indicate that the proposed real-time error correction method can effectively reduce the errors of forecasted water stages at the 1-, 2-, and 3-h lead time and so enhance the reliability of forecast information issued by the FEWS_Taiwan. By means of real-time estimating potential forecast error, the uncertainties in hydrology, modules as well as associated parameters, and physiographical features of the river can be reduced.     

Floodplain friction parameterization in two‐dimensional river flood models using vegetation heights derived from airborne scanning laser altimetry

Two‐dimensional (2‐D) hydraulic models are currently at the forefront of research into river flood inundation prediction. Airborne scanning laser altimetry is an important new data source that can provide such models with spatially distributed floodplain topography together with vegetation heights for parameterization of model friction. The paper investigates how vegetation height data can be used to realize the currently unexploited potential of 2‐D flood models to specify a friction factor at each node of the finite element model mesh. The only vegetation attribute required in the estimation of floodplain node friction factors is vegetation height. Different sets of flow resistance equations are used to model channel sediment, short vegetation, and tall and intermediate vegetation. The scheme was tested in a modelling study of a flood event that occurred on the River Severn, UK, in October 1998. A synthetic aperture radar image acquired during the flood provided an observed flood extent against which to validate the predicted extent. The modelled flood extent using variable friction was found to agree with the observed extent almost everywhere within the model domain. The variable‐friction model has the considerable advantage that it makes unnecessary the unphysical fitting of floodplain and channel friction factors required in the traditional approach to model calibration. Copyright © 2003 John Wiley & Sons, Ltd.     

Modelling Freshwater Resources at the Global Scale: Challenges and Prospects

Quantification of spatially and temporally resolved water flows and water storage variations for all land areas of the globe is required to assess water resources, water scarcity and flood hazards, and to understand the Earth system. This quantification is done with the help of global hydrological models (GHMs). What are the challenges and prospects in the development and application of GHMs? Seven important challenges are presented. (1) Data scarcity makes quantification of human water use difficult even though significant progress has been achieved in the last decade. (2) Uncertainty of meteorological input data strongly affects model outputs. (3) The reaction of vegetation to changing climate and CO₂ concentrations is uncertain and not taken into account in most GHMs that serve to estimate climate change impacts. (4) Reasons for discrepant responses of GHMs to changing climate have yet to be identified. (5) More accurate estimates of monthly time series of water availability and use are needed to provide good indicators of water scarcity. (6) Integration of gradient-based groundwater modelling into GHMs is necessary for a better simulation of groundwater–surface water interactions and capillary rise. (7) Detection and attribution of human interference with freshwater systems by using GHMs are constrained by data of insufficient quality but also GHM uncertainty itself. Regarding prospects for progress, we propose to decrease the uncertainty of GHM output by making better use of in situ and remotely sensed observations of output variables such as river discharge or total water storage variations by multi-criteria validation, calibration or data assimilation. Finally, we present an initiative that works towards the vision of hyperresolution global hydrological modelling where GHM outputs would be provided at a 1-km resolution with reasonable accuracy.     

Flood prediction using parameters calibrated on limited discharge data and uncertain rainfall scenarios

ABSTRACT

Discharge observations and reliable rainfall forecasts are essential for flood prediction but their availability and accuracy are often limited. However, even scarce data may still allow adequate flood forecasts to be made. Here, we explored how far using limited discharge calibration data and uncertain forcing data would affect the performance of a bucket-type hydrological model for simulating floods in a tropical basin. Three events above thresholds with a high and a low frequency of occurrence were used in calibration and 81 rainfall scenarios with different degrees of uncertainty were used as input to assess their effects on flood predictions. Relatively similar model performance was found when using calibrated parameters based on a few events above different thresholds. Flood predictions were sensitive to rainfall errors, but those related to volume had a larger impact. The results of this study indicate that a limited number of events can be useful for predicting floods given uncertain rainfall forecasts.     

Combining a rainfall–runoff model and a regionalization approach for flood and water resource assessment in the western Po Valley,Italy

ABSTRACT

Selecting the best structure and parameterization of rainfall–runoff models is not straightforward and depends on a broad number of factors. In this study, the “Modello Idrologico Semi-Distribuito in continuo” (MISDc) was tested on 63 mountainous catchments in the western Po Valley (Italy) and the optimal model parameters were regionalized using different strategies. The model performance was evaluated through several indexes analysing hydrological regime, high-flow condition and flow–duration curve (FDC). In general, MISDc provides a good fit behaviour with a Kling-Gupta Efficiency index greater than 0.5 for 100% and 84% of cases for calibration and validation, respectively. Concerning the regionalization, spatial proximity approach is the most accurate solution obtaining satisfactory performance. Lastly, the predicted FDCs showed an excellent similarity with the observed ones. Results encourage to apply MISDc over the study area for flood forecasting and for assessing water resources availability thanks to the modest computational efforts and data requirements.     

Impacts of massive pumping on flood processes in polders—with a case study on Lannihu basin in the Dongting Lake District,China

Flooding risk in polders is dictated by not only rainfall, topography, and land use, but also massive pumping. Unfortunately, existing models are inadequate for resolving floods as water transfer due to pumping is insufficiently accounted for. Here an improved hydrological model (MGB-MP) is proposed under the framework of the large-scale hydrological model (MGB) based on the principle of water balance, explicitly incorporating massive pumping within a polder and also out to external rivers. The proposed model is calibrated and validated for the Lannihu basin, a typical polder with an area of 1353 km² and 126 pumping stations in the Dongting Lake District, China and surrounded by Xiangjiang River and Zishui River. The model performs fairly well, with Nash-Sutcliffe efficiencies concerning water levels over 0.76 for the calibration and over 0.73 for the validation. The model is applied to the Lannihu basin under different pumping station settings and rainfall scenarios to unravel how and to what extent massive pumping affects the flood processes as characterized by water levels and discharge hydrographs. It is shown that massive pumping considerably alters the discharge hydrographs and accordingly leads to substantial decrease in the water levels of rivers, which are independent unit-polders, due to water transfer between unit-polders within the basin and out of the basin. The closer the unit-polders are to pumping stations, the more the water levels in unit-polders decrease. The water levels in unit-polders away from a pumping station is affected by the pumping station capacity to a greater extent than the pumping station's threshold water level for initiating pumping.     

Modélisation de propagation d’écoulement entre lits mineur et majeur sur les fleuves Sénégal et Niger

In the Senegal River valley and Niger Inner Delta, the annual floods inundate a wide floodplain consisting of a complex network of lakes and channels, where topographic information needed by standard hydraulic models is difficult to obtain. To represent the flood propagation between mainstream and floodplain, we use a model designed for flood propagation in river mainstreams with flat bed and large overflow and without topographic data. Depending on the water level in the riverbed, the model calibrated on the levels observed at two stations gives the level in the floodplains and propagation time between stations. Several cases are tested for various types of hydraulic connections between mainstream and floodplain. The model could correctly reproduce the flood rise and fall in the floodplain, even for a lake connected by a single channel to the riverbed or in the case of a strong attenuation of the flood between very distant stations.     

Characterisation of flood inundated areas and delineation of poor drainage soil using ERS-1 SAR imagery

Recent years have been marked by a continuous availability of spatial SAR data since the launch of the European remote sensing satellite (ERS-1) in 1991. Consequently, remote sensing techniques now offer an opportunity to map flood inundation fields caused by river overflow or waterlogging in environments characterized by frequent cloud cover. Indeed, inundation fields can clearly be seen on ERS-1 SAR images taken during flooding periods. However, such an identification can be constrained by the similarity in behaviour between water surfaces and other features of the landscape such as extended asphalt areas, permanent water bodies and less illuminated slopes. For consistent flood inundation extent mapping a more robust approach is required. This is provided by a conceptual flood inundation index that is physically sound in relation to radar imaging. Moreover, this index has proved to be useful for highlighting soils located within inundation fields and having significantly different internal drainage. The results achieved in the framework of the research must be seen in the context of intensive use of remote sensing data to support decision methods for sustainable management of land and water resources. Such decision support methods could be provided by river hydraulic models aimed at assessing environmental effects of inundation floods and at early flood warning systems. © 1997 John Wiley & Sons, Ltd.     

A data based mechanistic approach to nonlinear flood routing and adaptive flood level forecasting

Operational flood forecasting requires accurate forecasts with a suitable lead time, in order to be able to issue appropriate warnings and take appropriate emergency actions. Recent improvements in both flood plain characterization and computational capabilities have made the use of distributed flood inundation models more common. However, problems remain with the application of such models. There are still uncertainties associated with the identifiability of parameters; with the computational burden of calculating distributed estimates of predictive uncertainty; and with the adaptive use of such models for operational, real-time flood inundation forecasting. Moreover, the application of distributed models is complex, costly and requires high degrees of skill. This paper presents an alternative to distributed inundation models for real-time flood forecasting that provides fast and accurate, medium to short-term forecasts. The Data Based Mechanistic (DBM) methodology exploits a State Dependent Parameter (SDP) modelling approach to derive a nonlinear dependence between the water levels measured at gauging stations along the river. The transformation of water levels depends on the relative geometry of the channel cross-sections, without the need to apply rating curve transformations to the discharge. The relationship obtained is used to transform water levels as an input to a linear, on-line, real-time and adaptive stochastic DBM model. The approach provides an estimate of the prediction uncertainties, including allowing for heterescadasticity of the multi-step-ahead forecasting errors. The approach is illustrated using an 80 km reach of the River Severn, in the UK.     

Bayesian updating of flood inundation likelihoods conditioned on flood extent data

Previously we have detailed an application of the generalized likelihood uncertainty estimation (GLUE) procedure to estimate spatially distributed uncertainty in models conditioned against binary pattern data contained in flood inundation maps. This method was applied to two sites where a single consistent synoptic image of inundation extent was available to test the simulation performance of the method. In this paper, we extend this to examine the predictive performance of the method for a reach of the River Severn, west‐central England. Uniquely for this reach, consistent inundation images of two major floods have been acquired from spaceborne synthetic aperture radars, as well as a high‐resolution digital elevation model derived using laser altimetry. These data thus allow rigorous split sample testing of the previous GLUE application. To achieve this, Monte Carlo analyses of parameter uncertainty within the GLUE framework are conducted for a typical hydraulic model applied to each flood event. The best 10% of parameter sets identified in each analysis are then used to map uncertainty in flood extent predictions using the method previously proposed for both an independent validation data set and a design flood. Finally, methods for combining the likelihood information derived from each Monte Carlo ensemble are examined to determine whether this has the potential to reduce uncertainty in spatially distributed measures of flood risk for a design flood. The results show that for this reach and these events, the method previously established is able to produce sharply defined flood risk maps that compare well with observed inundation extent. More generally, we show that even single, poor‐quality inundation extent images are useful in constraining hydraulic model calibrations and that values of effective friction parameters are broadly stationary between the two events simulated, most probably reflecting their similar hydraulics. Copyright © 2004 John Wiley & Sons, Ltd.     

Investigating sea-state effects on flash flood hydrograph and inundation forecasting

A common source of uncertainty in flood inundation forecasting is the hydrograph used. Given the role of sea-air-hydro-land chain processes on the water cycle, flood hydrographs in coastal areas can be indirectly affected by sea state. This study investigates sea-state effects on precipitation, discharge, and flood inundation forecasting implementing atmospheric, ocean wave, hydrological, and hydraulic-hydrodynamic coupled models. The Chemical Hydrological Atmospheric Ocean wave System (CHAOS) was used for coupled hydro-meteorological-wave simulations ‘accounting’ or ‘not accounting’ the impact of sea state on precipitation and, subsequently, on flood hydrograph. CHAOS includes the WRF-Hydro hydrological model and the WRF-ARW meteorological model two-way coupled with the WAM wave model through the OASIS3-MCT coupler. Subsequently, the 2D HEC-RAS hydraulic-hydrodynamic model was forced by the flood hydrographs and map the inundated areas. A flash flood event occurred on 15 November 2017 in Mandra, Attica, Greece, causing 24 fatalities, and damages was selected as case study. The calibration of models was performed exploiting historical flood records and previous studies. Human interventions such as hydraulic works and the urban areas were included in the hydraulic modelling geometry domain. The representation of the resistance caused by buildings was based on Unmanned Aerial System (UAS) data while the local elevation rise method was used in the urban-flood simulation. The flood extent results were assessed using the Critical Success Index (CSI), and CSI penalize. Integrating sea-state affected the forecast of precipitation and discharge peaks, causing up to +24% and from −8% to +36% differences, respectively, improving inundation forecast by 4.5% and flooding additional approximately 70 building blocks. The precipitation forcing time step was also highlighted as significant factor in such a small-scale flash flood. The integrated multidisciplinary methodological approach could be adopted in operational forecasting for civil protection applications facilitating the protection of socio-economic activities and human lives during similar future events.