Evaluation of ASTER GDEM and SRTM and their suitability in hydraulic modelling of a glacial lake outburst flood in southeast Tibet

The resolution and accuracy of digital elevation models (DEMs) can affect the hydraulic simulation results for predicting the effects of glacial lake outburst floods (GLOFs). However, for the Tibetan Plateau, high‐quality DEM data are often not available, leaving researchers with near‐global, freely available DEMs, such as the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) and the Shuttle Radar Topography Mission data (SRTM) for hydraulic modelling. This study explores the suitability of these two freely available DEMs for hydraulic modelling of GLOFs. Our study focused on the flood plain of a potentially dangerous glacial lake in southeastern Tibet, to evaluate the elevation accuracy of ASTER GDEM and SRTM, and their suitability for hydraulic modelling of GLOFs. The elevation accuracies of ASTER GDEM and SRTM were first validated against field global position system (GPS) survey points, and then evaluated with reference to the relatively high precision of 1:50 000 scale DEM (DEM5) constructed from aerial photography. Moreover, the DEM5, ASTER GDEM and SRTM were used as basic topographic data to simulate peak discharge propagation, as well as flood inundation extent and depth in the Hydrologic Engineering Center's River Analysis System one‐dimensional hydraulic model. Results of the three DEM predictions were compared to evaluate the suitability of ASTER GDEM and SRTM for GLOF hydraulic modelling. Comparisons of ASTER GDEM and SRTM each with DEM5 in the flood plain area show root‐mean‐square errors between the former two as ± 15·4 m and between the latter two as ± 13·5 m. Although SRTM overestimates and ASTER GDEM underestimates valley floor elevations, both DEMs can be used to extract the elevations of required geometric data, i.e. stream centre lines, bank lines and cross sections, for flood modelling. However, small errors still exist in the cross sections that may influence the propagation of peak discharge. The flood inundation extent and mean water depths derived from ASTER GDEM predictions are only 2·2% larger and 2·3‐m deeper than that of the DEM5 predictions, whereas the SRTM yields a flood zone extent 6·8% larger than the DEM5 prediction and a mean water depth 2·4‐m shallower than the DEM5 prediction. The modelling shows that, in the absence of high‐precision DEM data, ASTER GDEM or SRTM DEM can be relied on for simulating extreme GLOFs in southeast Tibet. Copyright © 2011 John Wiley & Sons, Ltd.     

Estimation of uncertainty propagation in flood inundation mapping using a 1‐D hydraulic model

A need for more accurate flood inundation maps has recently arisen because of the increasing frequency and extremity of flood events. The accuracy of flood inundation maps is determined by the uncertainty propagated from all of the variables involved in the overall process of flood inundation modelling. Despite our advanced understanding of flood progression, it is impossible to eliminate the uncertainty because of the constraints involving cost, time, knowledge, and technology. Nevertheless, uncertainty analysis in flood inundation mapping can provide useful information for flood risk management. The twin objectives of this study were firstly to estimate the propagated uncertainty rates of key variables in flood inundation mapping by using the first‐order approximation method and secondly to evaluate the relative sensitivities of the model variables by using the Hornberger–Spear–Young (HSY) method. Monte Carlo simulations using the Hydrologic Engineering Center's River Analysis System and triangle‐based interpolation were performed to investigate the uncertainty arising from discharge, topography, and Manning's n in the East Fork of the White River near Seymour, Indiana, and in Strouds Creek in Orange County, North Carolina. We found that the uncertainty of a single variable is propagated differently to the flood inundation area depending on the effects of other variables in the overall process. The uncertainty was linearly/nonlinearly propagated corresponding to valley shapes of the reaches. In addition, the HSY sensitivity analysis revealed the topography of Seymour reach and the discharge of Strouds Creek to be major contributors to the change of flood inundation area. Copyright © 2014 John Wiley & Sons, Ltd.     

Applying ANN emulators in uncertainty assessment of flood inundation modelling: a comparison of two surrogate schemes

Abstract

A generalized likelihood uncertainty estimation (GLUE) framework coupling with artificial neural network (ANN) models in two surrogate schemes (i.e. GAE-S1 and GAE-S2) was proposed to improve the efficiency of uncertainty assessment in flood inundation modelling. The GAE-S1 scheme was to construct an ANN to approximate the relationship between model likelihoods and uncertain parameters for facilitating sample acceptance/rejection instead of running the numerical model directly; thus, it could speed up the Monte Carlo simulation in stochastic sampling. The GAE-S2 scheme was to establish independent ANN models for water depth predictions to emulate the numerical models; it could facilitate efficient uncertainty analysis without additional model runs for locations concerned under various scenarios. The results from a study case showed that both GAE-S1 and GAE-S2 had comparable performances to GLUE in terms of estimation of posterior parameters, prediction intervals of water depth, and probabilistic inundation maps, but with reduced computational requirements. The results also revealed that GAE-S1 possessed a slightly better performance in accuracy (referencing to GLUE) than GAE-S2, but a lower flexibility in application. This study shed some light on how to apply different surrogate schemes in using numerical models for uncertainty assessment, and could help decision makers in choosing cost-effective ways of conducting flood risk analysis.     

Quantifying the relative impact of hydrological and hydraulic modelling parameterizations on uncertainty of inundation maps

ABSTRACT

Flood risk management strongly relies on inundation models for river basin zoning in flood-prone and risk-free areas. Floodplain zoning is significantly affected by the diverse and concurrent uncertainties that characterize the modelling chain used for producing inundation maps. In order to quantify the relative impact of the uncertainties linked to a lumped hydrological (rainfall–runoff) model and a FLO-2D hydraulic model, a Monte Carlo procedure is proposed in this work. The hydrological uncertainty is associated with the design rainfall estimation method, while the hydraulic model uncertainty is associated with roughness parameterization. This uncertainty analysis is tested on the case study of the Marta coastal catchment in Italy, by comparing the different frequency, extent and depth of inundation simulations associated with varying rainfall forcing and/or hydraulic model roughness realizations. The results suggest a significant predominance of the hydrological uncertainty with respect to the hydraulic one on the overall uncertainty associated with the simulated inundation maps.     

Evaluation of on-line DEMs for flood inundation modeling

Recent and highly accurate topographic data should be used for flood inundation modeling, but this is not always feasible given time and budget constraints so the utility of several on-line digital elevation models (DEMs) is examined with a set of steady and unsteady test problems. DEMs are used to parameterize a 2D hydrodynamic flood simulation algorithm and predictions are compared with published flood maps and observed flood conditions. DEMs based on airborne light detection and ranging (LiDAR) are preferred because of horizontal resolution, vertical accuracy (∼0.1 m) and the ability to separate bare-earth from built structures and vegetation. DEMs based on airborne interferometric synthetic aperture radar (IfSAR) have good horizontal resolution but gridded elevations reflect built structures and vegetation and therefore further processing may be required to permit flood modeling. IfSAR and shuttle radar topography mission (SRTM) DEMs suffer from radar speckle, or noise, so flood plains may appear with non-physical relief and predicted flood zones may include non-physical pools. DEMs based on national elevation data (NED) are remarkably smooth in comparison to IfSAR and SRTM but using NED, flood predictions overestimate flood extent in comparison to all other DEMs including LiDAR, the most accurate. This study highlights utility in SRTM as a global source of terrain data for flood modeling.     

Low-cost inundation modelling at the reach scale with sparse data in the Lower Damodar River basin,India

Abstract

Data unavailability is the main reason for limited applications of hydrodynamic models for predicting inundation in the developing world. This paper aims to generate moderately high-resolution hybrid terrain data by merging height information from low-cost Indian Remote Sensing satellite (IRS) Cartosat-1 stereo satellite images, freely-available Shuttle Radar Topograph Mission (SRTM) digital elevation model (DEM) data, and limited surveyed channel cross-sections. The study reach is characterized by anabranching channels that are associated with channel bifurcation, loops and river islands. We compared the performance of a simple 1D–2D coupled LISFLOOD-FP model and a complex fully 2D finite element TELEMAC-2D model with the hybrid terrain data. The results show that TELEMAC-2D produced significantly improved simulated inundation with the hybrid terrain data, as compared to the SRTM DEM. LISFLOOD-FP was found unsuitable to work with the hybrid DEM in a complicated fluvial environment, as it failed to efficiently divert water in the branches from the main channel.

Editor D. Koutsoyiannis; Associate editor A. Viglione     

Rainfall–runoff,flood inundation and sensitivity analysis of the 2014 Pakistan flood in the Jhelum and Chenab river basin

ABSTRACT

The major flood of 2014 in the two eastern, transboundary rivers, the Jhelum and Chenab in Punjab, Pakistan, was simulated using the two-dimensional rainfall–runoff model. The simulated hydrograph showed good agreement with the observed discharge at the model outlet and intervening barrages, with a Nash-Sutcliffe efficiency of 0.86 at the basin outlet. Further, simulated flood inundation extent showed good agreement with the MODIS imagery with a fit (%) of 0.87. For some affected areas that experienced short-duration flooding, local housing damage data confirmed the simulated results. Besides the rainfall–runoff and flood inundation modelling, parameter sensitivity analysis was undertaken to identify the influence of various river and floodplain parameters. The analysis showed that the river channel geometric parameters and the roughness coefficients exerted the primary influence over flood extent and peak flow.     

Flood-plain mapping: a critical discussion of deterministic and probabilistic approaches

Abstract

Different methodologies for flood-plain mapping are analysed and discussed by comparing deterministic and probabilistic approaches using hydrodynamic numerical solutions. In order to facilitate the critical discussion, state-of-art techniques in the field of flood inundation modelling are applied to a specific test site (River Dee, UK). Specifically, different flood-plain maps are derived for this test site. A first map is built by applying an advanced deterministic approach: use of a fully two-dimensional finite element model (TELEMAC-2D), calibrated against a historical flood extent, to derive a 1-in-100 year flood inundation map. A second map is derived by using a probabilistic approach: use of a simple raster-based inundation model (LISFLOOD-FP) to derive an uncertain flood extent map predicting the 1-in-100 year event conditioned on the extent of the 2006 flood. The flood-plain maps are then compared and the advantages and disadvantages of the two different approaches are critically discussed.

Citation Di Baldassarre, G., Schumann, G., Bates, P. D., Freer, J. E. & Beven, K. J. (2010) Flood-plain mapping: a critical discussion of deterministic and probabilistic approaches. Hydrol. Sci. J. 55(3), 364–376.     

Effect of uncertainty in Digital Surface Models on the extent of inundated areas

The impact of uncertainty in ground elevation on the extent of areas that are inundated due to flooding is investigated. Land surface is represented through a Digital Surface Model (DSM). The effect of uncertainty in DSM is compared to that of the uncertainty due to rainfall. The Monte Carlo method is used to quantify the uncertainty. A typical photogrammetric procedure and conventional maps are used to obtain a reference DSM, later altered to provide DSMs of lower accuracy. Also, data from the Shuttle Radar Topography Mission are used. Floods are simulated in two stages. In the first stage, flood hydrographs for typical return periods are synthesized using generated storm hyetographs, the Soil Conservation Service–Curve Number method for effective rainfall, and the Soil Conservation Service synthetic unit hydrograph. In the second stage, hydrographs are routed via a one‐dimensional hydraulic model. Uncertainty in DSM is considered only in the second stage. Data from two real‐world basins in Greece are used. To characterize the inundated area, we employ the 90% quantile of the inundation extent and inundation topwidth for peak water level at specific river cross‐sections. For topwidths, apart from point estimates, also interval estimates are acquired using the bootstrap method. The effect of DSM uncertainty is compared to that for rainfall. Low uncertainty in DSM is found to widen the inundated area; whereas, the opposite occurred with high uncertainty. SRTM data proved unsuitable for our test basins and modelling context.     

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.     

A hydro‐economic modelling framework for flood damage estimation and the role of riparian vegetation

A modelling framework for the quick estimate of flood inundation and the resultant damages is developed in this paper. The model, called the flood economic impact analysis system (FEIAS), can be applied to a river reach of any hydrogeological river basin. For the development of the integrated modelling framework, three models were employed: (1) a modelling scheme based on the Hydrological Simulation Program FORTRAN model that was developed for any geomorphological river basin, (2) a river flow/floodplain model, and (3) a flood loss estimation model. The first sub‐model of the flood economic impact analysis system simulates the hydrological processes for extended periods of time, and its output is used as input to a second component, the river/floodplain model. The hydraulic model MIKE 11 (quasi‐2D) is the river/floodplain model employed in this study. The simulated flood parameters from the hydraulic model MIKE 11 (quasi‐2D) are passed, at the end of each time step, to a third component, the flood loss model for the estimation of flood damage. In the present work, emphasis was given to the seasonal variation of Manning's coefficient (n), which is an important parameter for the determination of the flood inundation in hydraulic modelling. High values of Manning's coefficient for a channel indicate high flow resistance. The riparian vegetation can have a large impact on channel resistance. The modelling framework developed in this paper was used to investigate the role of riparian vegetation in reducing flood damage. Moreover, it was used to investigate the influence of cutting riparian vegetation scenarios on the flow characteristics. The proposed framework was applied to the downstream part of the Koiliaris River basin in Crete, Greece, and was tested and validated with historical data. Copyright © 2012 John Wiley & Sons, Ltd.     

Author index 2002

Abstract

The effects of human activities on flood propagation, during the period 1878–2005, in a 190-km reach of the middle—lower portion of the River Po (Northern Italy) are investigated. A series of topographical, hydrological and inundation data were collected for the 1878 River Po geometry and the June 1879 flood event, characterised by an inundated area of 432 km². The aim of the study is two-fold: (1) to show the applicability of flood inundation models in reconstructing historical inundation events, and (2) to assess the effects of human activities during the last century on flood propagation in the middle—lower portion of the River Po. Numerical simulations were performed by coupling a two-dimensional finite element code, TELEMAC-2D, with a one-dimensional finite difference code, HEC-RAS.     

Improvement of a drainage system for flood management with assessment of the potential effects of climate change

Abstract

Runoff discharge in the Tuku lowlands, Taiwan, has increased with land development. Frequent floods caused by extreme weather conditions have resulted in considerable economic and social losses in recent years. Currently, numerous infrastructures have been built in the lowland areas that are prone to inundation; the measures and solutions for flood mitigation focus mainly on engineering aspects. Public participation in the development of principles for future flood management has helped both stakeholders and engineers. An integrated drainage–inundation model, combining a drainage flow model with a two-dimensional overland-flow inundation model is used to evaluate the flood management approaches with damage loss estimation. The proposed approaches include increasing drainage capacity, using fishponds as retention ponds, constructing pumping stations, and building flood diversion culverts. To assess the effects on the drainage system of projected increase of rainfall due to climate change, for each approach simulations were performed to obtain potential inundation extent and depth in terms of damage losses. The results demonstrate the importance of assessing the impacts of climate change for implementing appropriate flood management approaches.

Editor Z.W. Kundzewicz

Citation Chang, H.-K., Tan, Y.-C., Lai, J.-S., Pan, T.-Y., Liu, T.-M., and Tung, C.-P., 2013. Improvement of a drainage system for flood management with assessment of the potential effects of climate change. Hydrological Sciences Journal, 58 (8), 1581–1597.     

Assessing the uncertainty in distributed model predictions using observed binary pattern information within GLUE

In this paper we extend the generalized likelihood uncertainty estimation (GLUE) technique to estimate spatially distributed uncertainty in models conditioned against binary pattern data contained in flood inundation maps. Untransformed binary pattern data already have been used within GLUE to estimate domain‐averaged (zero‐dimensional) likelihoods, yet the pattern information embedded within such sources has not been used to estimate distributed uncertainty. Where pattern information has been used to map distributed uncertainty it has been transformed into a continuous function prior to use, which may introduce additional errors. To solve this problem we use here ‘raw’ binary pattern data to define a zero‐dimensional global performance measure for each simulation in a Monte Carlo ensemble. Thereafter, for each pixel of the distributed model we evaluate the probability that this pixel was inundated. This probability is then weighted by the measure of global model performance, thus taking into account how well a given parameter set performs overall. The result is a distributed uncertainty measure mapped over real space. The advantage of the approach is that it both captures distributed uncertainty and contains information on global likelihood that can be used to condition predictions of further events for which observed data are not available. The technique is applied to the problem of flood inundation prediction at two test sites representing different hydrodynamic conditions. In both cases, the method reveals the spatial structure in simulation uncertainty and simultaneously enables mapping of flood probability predicted by the model. Spatially distributed uncertainty analysis is shown to contain information over and above that available from global performance measures. Overall, the paper highlights the different types of information that may be obtained from mappings of model uncertainty over real and n‐dimensional parameter spaces. Copyright © 2002 John Wiley & Sons, Ltd.     

The potential application of finite element modelling of flood plain inundation to predict patterns of overbank deposition

Abstract

Mathematical modelling of overbank inundation and flows faces many problems and is still in its infancy. Work to date has generally been restricted to small reaches. Large-scale models based on longer reaches of river channel are likely to be of greater value for engineering and flood plain management purposes, but the problems associated with the transition from small to large scales need to be assessed. A large-scale finite element model, RMA-2, has been applied to the flood plain of the lower reaches of the River Culm in southeast Devon, UK. Patterns of radiocaesium accumulation by overbank accretion during flood water inundation were used to assess the potential of using such models for explaining sedimentation rates and patterns. A strong correlation was found between values of the ¹³⁷Cs inventory and surface concentration and the predicted flood water patterns derived using the RMA-2 model. Except where recession pondage occurs, an inverse relationship existed between ¹³⁷Cs deposition and water depth. However, the discretization model developed cannot presently cope with large-scale compartmentalization of flows by barriers to flow and small-scale local features, such as ditches crossing the flood plain and the microtopography of the flood plain. This study appraises the potential for using the RMA-2 model to predict patterns of overbank deposition and represents an initial stage in the development of an integrated model of hydraulic and sediment dynamics.     

Timely Low Resolution SAR Imagery To Support Floodplain Modelling: a Case Study Review

It is widely recognised that remote sensing can support flood monitoring, modelling and management. In particular, satellites carrying Synthetic Aperture Radar (SAR) sensors are valuable as radar wavelengths can penetrate cloud cover and are insensitive to daylight. However, given the strong inverse relationship between spatial resolution and revisit time, monitoring floods from space in near real time is currently only possible through low resolution (about 100 m pixel size) SAR imagery. For instance, ENVISAT-ASAR (Advanced Synthetic Aperture Radar) in WSM (wide swath mode) revisit times are of the order of 3 days and the data can be obtained within 24 h at no (or low) cost. Hence, this type of space-borne data can be used for monitoring major floods on medium-to-large rivers. This paper aims to discuss the potential for, and uncertainties of, coarse resolution SAR imagery to monitor floods and support hydraulic modelling. The paper first describes the potential of globally and freely available space-borne data to support flood inundation modelling in near real time. Then, the uncertainty of SAR-derived flood extent maps is discussed and the need to move from deterministic binary maps (wet/dry) of flood extent to uncertain flood inundation maps is highlighted.     

An event simulation approach to the assessment of flood level frequencies: risk maps for the Warsaw reach of the River Vistula

Flood risk assessment is customarily performed using a design flood. Observed past flows are used to derive a flood frequency curve which forms the basis for a construction of a design flood. The simulation of a distributed model with the 1‐in‐T year design flood as an input gives information on the possible inundation areas, which are used to derive flood risk maps. The procedure is usually performed in a deterministic fashion, and its extension to take into account the design flood‐and flow routing model uncertainties is computer time consuming. In this study we propose a different approach to flood risk assessment which consists of the direct simulation of a distributed flow routing model for an observed series of annual maximum flows and the derivation of maps of probability of inundation of the desired return period directly from the obtained simulations of water levels at the model cross sections through an application of the Flood Level Frequency Analysis. The hydraulic model and water level quantile uncertainties are jointly taken into account in the flood risk uncertainty evaluation using the Generalized Likelihood Uncertainty Estimation (GLUE) approach. An additional advantage of the proposed approach lies in smaller uncertainty of inundation predictions for long return periods compared to the standard approach. The approach is illustrated using a design flood level and a steady‐state solution of a hydraulic model to derive maps of inundation probabilities. Copyright © 2016 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.     

Comparing 2D capabilities of HEC-RAS and LISFLOOD-FP on complex topography

ABSTRACT

This study evaluates and compares two-dimensional (2D) numerical models of different complexities by testing them on a floodplain inundation event that occurred on the Secchia River (Italy). We test 2D capabilities of LISFLOOD-FP and HEC-RAS (5.0.3), implemented using various grid sizes (25–100 m) based on 1-m DEM resolution. As expected, the best results were shown by the higher-resolution grids (25 m) for both models, which is justified by the complex terrain of the area. However, the coarser resolution simulations (50 and 100 m) performed virtually identically compared to the high-resolution simulations. Nevertheless, the spatial distribution of flood characteristics varies: the 50 and 100 m results of LISFLOOD-FP and HEC-RAS misestimated flood extent and water depth in selected control areas (built-up zones). We suggest that the specific terrain of the area can cause ambiguities in large-scale modelling, while providing plausible results in terms of the overall model performance.