When does spatial resolution become spurious in probabilistic flood inundation predictions?

Advances in remote sensing have enabled hydraulic models to run at fine scale resolutions, producing precise flood inundation predictions. However, running models at finer resolutions increase their computational expense, reducing the feasibility of running the multiple model realizations required to undertake uncertainty analysis. Furthermore, it is possible that precision gained by running fine scale models is smoothed out when treating models probabilistically. The aim of this paper is to determine the level of spatial complexity that is required when making probabilistic flood inundation predictions. The Imera basin, Sicily is used as a case study to assess how changing the spatial resolution of the hydraulic model LISFLOOD‐FP impacts on the skill of conditional probabilistic flood inundation maps given model parameter and boundary condition uncertainties. We find that model performance deteriorates at resolutions coarser than 50 m. This is predominantly caused by changes in flow pathways at coarser resolutions which lead to non‐stationarity in the optimum model parameters at different spatial resolutions. However, although it is still possible to produce probabilistic flood maps that contain a coherent outline of the flood extent at coarser resolutions, the reliability of these maps deteriorates at resolutions coarser than 100 m. Additionally, although the rejection of non‐behavioural models reduces the uncertainty in probabilistic flood maps the reliability of these maps is also reduced. Models with resolutions finer than 50 m offer little gain in performance yet are more than an order of magnitude computationally expensive which can become infeasible when undertaking probabilistic analysis. Furthermore, we show that using deterministic, high‐resolution flood maps can lead to a spurious precision that would be misleading and not representative of the overall uncertainties that are inherent in making inundation predictions. Copyright © 2015 The Authors Hydrological Processes Published by John Wiley & Sons Ltd.     

Spatially distributed observations in constraining inundation modelling uncertainties

The performance of two modelling approaches for predicting floodplain inundation is tested using observed flood extent and 26 distributed floodplain level observations for the 1997 flood event in the town of Usti nad Orlici in the Czech Republic. Although the one‐dimensional hydrodynamic model and the integrated one‐ and two‐dimensional model are shown to perform comparably against the flood extent data, the latter shows better performance against the distributed level observations. Comparable performance in predicting the extent of inundation is found to be primarily as a result of the urban reach considered, with flood extent constrained by road and railway embankments. Uncertainty in the elevation model used in both approaches is shown to have little effect on the reliability in predicting flood extent, with a greater impact on the ability in predicting the distributed level observations. These results show that reliability of flood inundation modelling in urban reaches, where flood risk assessment is of more interest than in more rural reaches, can be improved greatly if distributed observations of levels in the floodplain are used in constraining model uncertainties. Copyright © 2005 John Wiley & Sons, Ltd.     

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.     

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.     

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.     

Uncertainty and equifinality in calibrating distributed roughness coefficients in a flood propagation model with limited data

Monte-Carlo simulations of a two-dimensional finite element model of a flood in the southern part of Sicily were used to explore the parameter space of distributed bed-roughness coefficients. For many real-world events specific data are extremely limited so that there is not only fuzziness in the information available to calibrate the model, but fuzziness in the degree of acceptability of model predictions based upon the different parameter values, owing to model structural errors. Here the GLUE procedure is used to compare model predictions and observations for a certain event, coupled with both a fuzzy-rule-based calibration, and a calibration technique based upon normal and heteroscedastic distributions of the predicted residuals. The fuzzy-rule-based calibration is suited to an event of this kind, where the information about the flood is highly uncertain and arises from several different types of observation. The likelihood (relative possibility) distributions predicted by the two calibration techniques are similar, although the fuzzy approach enabled us to constrain the parameter distributions more usefully, to lie within a range which was consistent with the modellers' a priori knowledge of the system.     

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.     

A probabilistic model for real‐time flood warning based on deterministic flood inundation mapping

Deterministic flood inundation mapping is valuable for the investigation of detailed flood depth and extent. However, when these data are used for real‐time flood warning, uncertainty arises while encountering the difficulties of timely response, message interpretation and performance evaluation that makes statistical analysis necessary. By incorporating deterministic flood inundation map outputs statistically by means of logistic regression, this paper presents a probabilistic real‐time flood warning model determining region‐based flood probability directly from rainfall, being efficient in computation, clear in message, and valid in physical meaning. The calibration and validation of the probabilistic model show a satisfactory overall correctness rate, with the hit rate far surpassing the false alarm rate in issuing flood warning for historical events. Further analyses show that the probabilistic model is effective in evaluating the level of uncertainty lying within flood warning which can be reduced by several techniques proposed in order to improve warning performance. Copyright © 2011 John Wiley & Sons, Ltd.     

Joint Monte Carlo and possibilistic simulation for flood damage assessment

A joint Monte Carlo and fuzzy possibilistic simulation (MC-FPS) approach was proposed for flood risk assessment. Monte Carlo simulation was used to evaluate parameter uncertainties associated with inundation modeling, and fuzzy vertex analysis was applied for promulgating human-induced uncertainty in flood damage estimation. A study case was selected to show how to apply the proposed method. The results indicate that the outputs from MC-FPS would present as fuzzy flood damage estimate and probabilistic-possibilistic damage contour maps. The stochastic uncertainty in the flood inundation model and fuzziness in the depth-damage functions derivation would cause similar levels of influence on the final flood damage estimate. Under the worst scenario (i.e. a combined probabilistic and possibilistic uncertainty), the estimated flood damage could be 2.4 times higher than that computed from conventional deterministic approach; considering only the pure stochastic effect, the flood loss would be 1.4 times higher. It was also indicated that uncertainty in the flood inundation modeling has a major influence on the standard deviation of the simulated damage, and that in the damage-depth function has more notable impact on the mean of the fitted distributions. Through applying MC-FPS, rich information could be derived under various α-cut levels and cumulative probabilities, and it forms an important basis for supporting rational decision making for flood risk management under complex uncertainties.     

Dynamic flood topographies in the Terai region of Nepal

Flood hazard maps used to inform and build resilience in remote communities in the Terai region of southern Nepal are based on outdated and static digital elevation models (DEMs), which do not reflect dynamic river configuration or hydrology. Episodic changes in river course, sediment dynamics, and the distribution of flow down large bifurcation nodes can modify the extent of flooding in this region, but these processes are rarely considered in flood hazard assessment. Here, we develop a 2D hydrodynamic flood model of the Karnali River in the Terai region of west Nepal. A number of scenarios are tested examining different DEMs, variable bed elevations to simulate bed aggradation and incision, and updating bed elevations at a large bifurcation node to reflect field observations. By changing the age of the DEM used in the model, a 9.5% increase in inundation extent was observed for a 20-year flood discharge. Reducing horizontal DEM resolution alone resulted in a <1% change. Uniformly varying the bed elevation led to a 36% change in inundation extent. Finally, changes in bed elevation at the main bifurcation to reflect observed conditions resulted in the diversion of the majority of flow into the west branch, consistent with measured discharge ratios between the two branches, and a 32% change in inundation extent. Although the total flood inundation area was reduced (−4%), there was increased inundation along the west bank. Our results suggest that regular field measurements of bed elevation and updated DEMs following large sediment-generating events, and at topographically sensitive areas such as large river bifurcations, could help improve model inputs in future flood prediction models. This is particularly important following flood events carrying large sediment loads out of mountainous regions that could promote bed aggradation and channel switching across densely populated alluvial river systems and floodplains further downstream. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd     

Interactions between subgrid‐scale resolution,feature representation and grid‐scale resolution in flood inundation modelling

Numerical modelling of flood inundation over large and complex floodplains often requires mesh resolutions coarser than the structural features (e.g. buildings) that are known to influence the inundation process. Recent research has shown that this mismatch is not well represented by conventional roughness treatments, but that finer‐scale features can be represented through porosity‐based subgrid‐scale treatments. This paper develops this work by testing the interactions between feature representation, subgrid‐scale resolution and mesh resolution. It uses as the basis for this testing a 2D diffusion‐based flood inundation model which is applied to a 2004 flood event in a topologically complex upland floodplain in northern England. This study formulated simulations with different grid mesh resolution and subgrid mesh ratio. The sensitivity of the model to mesh resolution and roughness specification was investigated. Model validation and verification suggest that the subgrid treatment with higher subgrid mesh ratio can give much improved predictions of flood propagation, in particular, in terms of the predicted water depth. This study also highlighted the limitation of using at‐a‐point in time inundation extent for validation of flood models of this type. Copyright © 2010 John Wiley & Sons, Ltd.     

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.     

Reservoir storage for managing floods in urban areas: a case study of Dzorwulu basin in Accra

The study simulated the effect of using reservoir storage for reducing flood peaks and volumes in urban areas with the Dzorwulu basin in Accra, Ghana as case study. A triangulated irregular network surface of the floodplain was created using ArcGIS from ESRI by integrating digital elevation model and the map of the study area. The weighted curve number for the basin was obtained from the land use and soil type shape files using ArcGIS. The Soil Conservation Service curve number unit hydrograph procedure was used to obtain an inflow hydrograph based on the highest rainfall recorded in recent history (3–4 June 1995) in the study area and then routed through an existing reservoir to assess the impact of the reservoir on potential flood peak attenuation. The results from the analysis indicate that a total of 13.09 × 10⁶ m³ of flood water was generated during this 10‐h rainstorm, inundating a total area of 6.89 km² with a depth of 4.95 m at the deepest section of the basin stream. The routing results showed that the reservoir has capacity to store 34.52% of the flood hydrograph leading to 45% reduction in flood peak and subsequently 38.5% reduction in flood inundation depth downstream of the reservoir. From results of the study, the reservoir storage concept looks promising for urban flood management in Ghana, especially in communities that are over‐urbanized downstream but have some space upstream for creating the storage. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

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.     

洪水概率预报评价指标研究

在分析现有洪水概率预报评价指标的基础上,建立了洪水概率预报的"精度-可靠度"联合评价指标体系"精度"指标用于评价倾向值预报的准确性,包括确定性系数、相对误差等指标;"可靠度"指标用于评价区间预报的合理性,包括覆盖率、区间离散度等指标其中,在"可靠度"评价指标中,提出了一个新的评价指标,即覆盖率判定系数,用以评价多个区间预报结果的整体合理性以淮河王家坝区间流域为例进行应用研究,结果表明:"精度-可靠度"联合评价指标体系可以对洪水概率预报结果的合理性做出更全面的评价.     

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.     

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.     

Using atmospheric tracers to reduce uncertainty in groundwater recharge areas

A Monte Carlo-based approach to assess uncertainty in recharge areas shows that incorporation of atmospheric tracer observations (in this case, tritium concentration) and prior information on model parameters leads to more precise predictions of recharge areas. Variance-covariance matrices, from model calibration and calculation of sensitivities, were used to generate parameter sets that account for parameter correlation and uncertainty. Constraining parameter sets to those that met acceptance criteria, which included a standard error criterion, did not appear to bias model results. Although the addition of atmospheric tracer observations and prior information produced similar changes in the extent of predicted recharge areas, prior information had the effect of increasing probabilities within the recharge area to a greater extent than atmospheric tracer observations. Uncertainty in the recharge area propagates into predictions that directly affect water quality, such as land cover in the recharge area associated with a well and the residence time associated with the well. Assessments of well vulnerability that depend on these factors should include an assessment of model parameter uncertainty. A formal simulation of parameter uncertainty can be used to delineate probabilistic recharge areas, and the results can be expressed in ways that can be useful to water-resource managers. Although no one model is the correct model, the results of multiple models can be evaluated in terms of the decision being made and the probability of a given outcome from each model.     

Flood hydrograph reconstruction from the peak flow value in ephemeral streams using a simplified robust single‐parameter model

Flood hydrographs from ephemeral streams in arid areas provide valuable information for assessing run‐off and groundwater recharge. However, such data are often scarce or incomplete, especially in hyper‐arid regions. The hypothesis of this study was that it is possible to reconstruct a hydrograph of a specific point along an ephemeral stream with the knowledge of only the peak flow rate of a flood event at that point and that this can be done at almost every point along the stream. The feasibility of this approach lies in the shape of the recession stage of the flood hydrograph, which is known to be a repeating phenomenon. The recession stage comes immediately after the peak flow rate, when it begins its decline, and lasts until the flood is extinguished. A general shape of the flood recession stage can be provided. Because the recession stage represents ~80% of the duration of a flood event, it can provide a general idea of the flood hydrograph's shape. A simple model based on geometric progression is suggested to describe the repeating recession stage of a flood. The advantage of the proposed model is that it requires only one parameter: the recession characteristic at a fixed point along the ephemeral stream, termed recession coefficient q. By knowing the recession coefficient of a fixed point and the peak flow rate of a flood event at that point, one can plot the flood hydrograph. A good agreement is shown between the observed and computed values of the recession stage. Copyright © 2016 John Wiley & Sons, Ltd.