Discharge Uncertainty Research Articles

Greenland’s contribution to global sea-level rise by the end of the 21st century

The Greenland ice sheet holds enough water to raise the global sea level with *7 m. Over the last few decades, observations manifest a substantial increase of the mass loss of this ice sheet. Both enhanced melting and increase of the dynamical discharge, associated with calving at the outlet-glacier fronts, are contributing to the mass imbalance. Using a dynamical and thermodynamical ice-sheet model, and taking into account speed up of outlet glaciers, we estimate Greenland's contribution to the 21st- century global sea-level rise and the uncertainty of this estimate. Boundary fields of temperature and precipitation extracted from coupled climate-model projections used for the IPCC Fourth Assessment Report, are applied to the ice- sheet model. We implement a simple parameterization for increased flow of outlet glaciers, which decreases the bias of the modeled present-day surface height. It also allows for taking into account the observed recent increase in dynamical discharge, and it can be used for future pro- jections associated with outlet-glacier speed up. Greenland contributes 0-17 cm to global sea-level rise by the end of the 21st century. This range includes the uncertainties in climate-model projections, the uncertainty associated with scenarios of greenhouse-gas emissions, as well as the uncertainties in future outlet-glacier discharge. In addition, the range takes into account the uncertainty of the ice-sheet model and its boundary fields.

Stage‐discharge uncertainty derived with a non‐stationary rating curve in the Choluteca River, Honduras

AbstractUncertainty in discharge data must be critically assessed before data can be used in, e.g. water resources estimation or hydrological modelling. In the alluvial Choluteca River in Honduras, the river‐bed characteristics change over time as fill, scour and other processes occur in the channel, leading to a non‐stationary stage‐discharge relationship and difficulties in deriving consistent rating curves. Few studies have investigated the uncertainties related to non‐stationarity in the stage‐discharge relationship. We calculated discharge and the associated uncertainty with a weighted fuzzy regression of rating curves applied within a moving time window, based on estimated uncertainties in the observed rating data. An 18‐year‐long dataset with unusually frequent ratings (1268 in total) was the basis of this study. A large temporal variability in the stage‐discharge relationship was found especially for low flows. The time‐variable rating curve resulted in discharge estimate differences of − 60 to + 90% for low flows and ± 20% for medium to high flows when compared to a constant rating curve. The final estimated uncertainty in discharge was substantial and the uncertainty limits varied between − 43 to + 73% of the best discharge estimate. Copyright © 2010 John Wiley & Sons, Ltd.

Evaluation of the subjective factors of the GLUE method and comparison with the formal Bayesian method in uncertainty assessment of hydrological models

Quantification of uncertainty of hydrological models has attracted much attention in the recent hydrological literature. Different results and conclusions have been reported which result from the use of different methods with different assumptions. In particular, the disagreement between the Generalized Likelihood Uncertainty Estimation (GLUE) and the Bayesian methods for assessing the uncertainty in conceptual watershed modelling has been widely discussed. What has been mostly criticized is the subjective choice as regards the influence of threshold value, number of sample simulations, and likelihood function in the GLUE method. In this study the impact of threshold values and number of sample simulations on the uncertainty assessment of GLUE is systematically evaluated, and a comprehensive evaluation about the posterior distribution, parameter and total uncertainty estimated by GLUE and a formal Bayesian approach using the Metropolis Hasting (MH) algorithm are performed for two well-tested conceptual hydrological models (WASMOD and DTVGM) in an arid basin from North China. The results show that in the GLUE method, the posterior distribution of parameters and the 95% confidence interval of the simulated discharge are sensitive to the choice of the threshold value as measured by the acceptable samples rate (ASR). However, when the threshold value in the GLUE method is high enough (i.e., when the ASR value is smaller than 0.1%), the posterior distribution of parameters, the 95% confidence interval of simulated discharge and the percent of observations bracketed by the 95% confidence interval (P-95CI) for the GLUE method approach those values estimated by the Bayesian method for both hydrological models. Second, in the GLUE method, the insufficiency of number of sample simulations will influence the maximum Nash-Sutcliffe (MNS) efficiency value when ASR is fixed. However, as soon as the number of sample simulations increases to 2 x 10(4) for WASMOD and to 8 x 10(4) for the DTVGM model the influence of number of sample simulations on the model simulation results becomes of minor importance. Third, the uncertainty in simulated discharges resulting from parameter uncertainty is much smaller than that resulting from the model structure uncertainty for both hydrological models. Fourth, the goodness of model fit as measured by the maximum Nash-Sutcliffe efficiency value is nearly the same for the GLUE and the Bayesian methods for both hydrological models. Thus this study provides useful information on the uncertainty assessment of hydrological models. (C) 2010 Elsevier B.V. All rights reserved.

Fuzzy set theory based methodology for the analysis of measurement uncertainties in river discharge and stage

The discharge and stage measurements in a river system are characterized by a number of sources of uncertainty, which affects the accuracy of a rating curve established from measurements. This paper presents a fuzzy set theory based methodology for consideration of different sources of uncertainty in the stage and discharge measurements and their aggregation into a combined uncertainty. The uncertainty in individual measurements of stage and discharge is represented using triangular fuzzy numbers, and their spread is determined according to the International Organization for Standardization (ISO) standard 748 guidelines. The extension principle based fuzzy arithmetic is used for the aggregation of various uncertainties into overall stage–discharge measurement uncertainty. In addition, a fuzzified form of ISO 748 formulation is used for the calculation of combined uncertainty and comparison with the fuzzy aggregation method. The methodology developed in this paper is illustrated with a case study of the Thompson River near Spences Bridge in British Columbia, Canada. The results of the case study show that the selection of number of velocity measurement points on a vertical is the largest source of uncertainty in discharge measurement. An increase in the number of velocity measurement points provides the most effective reduction in the overall uncertainty. The next most important source of uncertainty for the case study location is the number of verticals used for velocity measurements. The study also shows that fuzzy set theory provides a suitable methodology for the uncertainty analysis of stage–discharge measurements.

A data assimilation approach to discharge estimation from space

AbstractRiver discharge is currently monitored by a diminishing network of gauges, which provide a spatially incomplete picture of global discharges. This study assimilated water level information derived from a fused satellite Synthetic Aperture Radar (SAR) image and digital terrain model (DTM) with simulations from a coupled hydrological and hydrodynamic model to estimate discharge in an un‐gauged basin scenario. Assimilating water level measurements led to a 79% reduction in ensemble discharge uncertainty over the coupled hydrological hydrodynamic model alone. Measurement bias was evident, but the method still provided a means of improving estimates of discharge for high flows. The study demonstrates the potential of currently available synthetic aperture radar imagery to reduce discharge uncertainty in un‐gauged basins when combined with model simulations in a data assimilation framework, where sufficient topographic data are available. The work is timely because in the near future the launch of satellite radar missions will lead to a significant increase in the volume of data available for space‐borne discharge estimation. Copyright © 2009 John Wiley & Sons, Ltd.

Effects of Precipitation Uncertainty on Discharge Calculations for Main River Basins

Abstract This study quantifies the uncertainty in discharge calculations caused by uncertainty in precipitation input for 294 river basins worldwide. Seven global gridded precipitation datasets are compared at river basin scale in terms of mean annual and seasonal precipitation. The representation of seasonality is similar in all datasets, but the uncertainty in mean annual precipitation is large, especially in mountainous, arctic, and small basins. The average precipitation uncertainty in a basin is 30%, but there are strong differences between basins. The effect of this precipitation uncertainty on mean annual and seasonal discharge was assessed using the uncalibrated dynamic global vegetation and hydrology model Lund–Potsdam–Jena managed land (LPJmL), yielding even larger uncertainties in discharge (average 90%). For 95 basins (out of 213 basins for which measurements were available) calibration of model parameters is problematic because the observed discharge falls within the uncertainty of the simulated discharge. A method is presented to account for precipitation uncertainty in discharge simulations.

A multistage multicriteria spatial sampling strategy for estimating contaminant mass discharge and its uncertainty

Mass discharge across a control plane has great potential to serve as a metric for the assessment of the impact of partial source zone depletion and for the development of risk‐based source plume remediation strategies. However, field‐estimated mass discharge is always subject to uncertainty, arising from nonexclusive sampling. The accuracy of the estimated discharge and the magnitude of its quantifiable uncertainty depend upon the amount of information provided by the sample data. A multistage spatial sampling strategy is proposed to select optimal sampling locations and determine minimal sampling density for accurate quantification of mass discharge uncertainty. Two sampling criteria are incorporated to ensure coverage of the control plane and delineation of highly concentrated areas (hot spots). Multiple criteria decision making theory is adapted to objectively weight the two sampling criteria, on the basis of the information importance of each criterion, with additional observations located according to the weighted average of the two criteria. Application of this methodology to numerically simulated plume transects shows that in comparison to one stage sampling design (regular pattern), this sampling strategy yields a 50% reduction in required sampling density for accurate uncertainty modeling. The developed sampling algorithm can be used in real time to guide staged field sampling.

Fuzzy Nonlinear Regression Approach to Stage-Discharge Analyses: Case Study

River discharge is typically derived from a single valued stage-discharge relationship. However, the relationship is affected by different sources of uncertainty, especially, in the measurement of discharge and stage values. The measurement uncertainty propagates into stage-discharge relationship curve and affects the discharge values derived from the relation. A fuzzy set theory based methodology is investigated in this paper for the analysis of uncertainty in the stage-discharge relationship. Individual components of stage and discharge measurement are considered as a fuzzy numbers and the overall stage and discharge uncertainty is obtained through the aggregation of all uncertainties using fuzzy arithmetic. Building on the previous work—fuzzy discharge and stage measurements, we use fuzzy nonlinear regression—in this case study for the analysis of uncertainty in the stage-discharge relationship. The methodology is based on fuzzy extension principle and considers input and output variables as well as th...

Field Calibration of Submerged Sluice Gates in Irrigation Canals

Four rectangular sluice gates were calibrated for submerged-flow conditions using nearly 16,000 field-measured data points on Canal B of the B-XII irrigation scheme in Lebrija, Spain. Water depth and gate opening values were measured using acoustic sensors at each of the gate structures, and the data were recorded on electronic data loggers. Several gate calibration equations were tested and it was found that the rectangular sluice gates can be used for accurate flow measurement. The Energy-Momentum (E-M) equations proved to be sound. The calibration of the contraction coefficient, to be used in the energy equation, allowed good estimations of the discharge for three of the four gates studied. The gate for which the E-M method did not perform satisfactorily was located at the head of the canal with a unique nonsymmetric approach flow condition. Alternatively, we investigated the performance of the conventional discharge equation. The variation of the discharge coefficient, Cd, with the head differential, Δh, and the vertical gate opening, w, suggests that Cd be expressed as a function of these two variables. For the sluice gates considered in this study, the best empirical fit was obtained by expressing Cd as a parabolic function of w, although an exponential expression tested previously by other writers also produced satisfactory results. The greatest uncertainty in the variables considered in this study was in the calculated coefficient of discharge, and based on the uncertainty analysis, it is possible to quantify the uncertainty in the estimated discharge through a calibrated sluice gate. The discharge uncertainty in each of the four gates in this study decreases with increasing gate opening, and it decreases slightly with increasing head differentials.

Predicting Model Uncertainty at River Junctions due to Drainage Network Structure

The mixing of water, solutes, and sediments from two or more tributaries produces streamflow possessing unique biogeochemical, ecological, hydrological, and morphological signatures. To understand and predict these signatures, confluence locations within river networks are commonly used to divide a watershed into individual modeling units, within these units hydrologic characteristics are spatially averaged and used to parameterize process models. In this context, model parameters from an individual unit directly influence the simulated hydrograph at its corresponding downstream junction and reach. Previous researchers have shown complex changes in flow hydraulics, stream geometry, and channel morphology in the downstream direction of a drainage network. However, the uncertainty of model predictions at and between junction locations due to parameter variability within individual modeling units is poorly understood. This paper presents a methodology for identifying stream junctions and reaches that are potentially vulnerable to increased model uncertainty due to the structural arrangement of their upstream drainage network and model parameter variability. A case study, using coastal southern California, is presented to illustrate the concepts and explore the relationships between changes in simulated peak discharge at junctions and along reaches due to expected model parameter variability for varying drainage network characteristics. Based on simulation results for 300 different drainage network configurations, eight model parameter scenarios, and three design storms (7,200 simulations), the uncertainty in simulated peak discharge within a reach is related to the drainage network characteristics. In the study region, 18% of the reaches are shown to be especially vulnerable to model parameter uncertainty, with 32% of those researches crossed by a major road and/or railroad. This approach is intended to improve risk assessment in sensitive hydrologic applications, such as the design of bridges, culverts, and channels to convey flood flows.

Estimating storm discharge and water quality data uncertainty: A software tool for monitoring and modeling applications

Uncertainty estimates corresponding to measured hydrologic and water quality data can contribute to improved monitoring design, decision-making, model application, and regulatory formulation. With these benefits in mind, the Data Uncertainty Estimation Tool for Hydrology and Water Quality (DUET-H/WQ) was developed from an existing uncertainty estimation framework for small watershed discharge, sediment, and N and P data. Both the software and its framework-basis utilize the root mean square error propagation methodology to provide uncertainty estimates instead of more rigorous approaches requiring detailed statistical information, which is rarely available. DUET-H/WQ lists published uncertainty information for data collection procedures to assist the user in assigning appropriate data-specific uncertainty estimates and then calculates the uncertainty for individual discharge, concentration, and load values. Results of DUET-H/WQ application in several studies indicated that substantial uncertainty can be contributed by each procedural category (discharge measurement, sample collection, sample preservation/storage, laboratory analysis, and data processing and management). For storm loads, the uncertainty was typically least for discharge (±7–23%), greater for sediment (±16–27%) and dissolved N and P (±14–31%) loads, and greater yet for total N and P (±18–36%). When these uncertainty estimates for individual values were aggregated within study periods (i.e. total discharge, average concentration, and total load), uncertainties followed the same pattern ( Q < TSS < dissolved N and P < total N and P). This rigorous demonstration of uncertainty in discharge and water quality data illustrates the importance of uncertainty analysis and the need for appropriate tools. It is our hope that DUET-H/WQ contributes to making uncertainty estimation a routine data collection and reporting procedure and thus enhances environmental monitoring, modeling, and decision-making. Hydrologic and water quality data are too important for scientists to continue to ignore the inherent uncertainty.

Impacts of permafrost degradation on arctic river biogeochemistry

AbstractOver the next century, near‐surface permafrost across the circumpolar Arctic is expected to degrade significantly, particularly for land areas south of 70°N. This is likely to cause widespread impacts on arctic hydrology, ecology, and trace gas emissions. Here, we present a review of recent studies investigating linkages between permafrost dynamics and river biogeochemistry in the Arctic, including consideration of likely impacts that warming‐induced changes in permafrost may be having (or will have in the future) on the delivery of organic matter, inorganic nutrients, and major ions to the Arctic Ocean. These interacting processes can be highly complex and undoubtedly exhibit spatial and temporal variabilities associated with current permafrost conditions, sensitivity to permafrost thaw, mode of permafrost degradation (overall permafrost thaw, active layer deepening, and/or thermokarst processes), and environmental characteristics of watersheds (e.g. land cover, soil type, and topography). One of the most profound consequences of permafrost thaw projected for the future is that the arctic terrestrial freshwater system is likely to experience a transition from a surface water‐dominated system to a groundwater‐dominated system. Along with many other cascading impacts from this transition, mineral‐rich groundwater may become an important contributor to streamflow, in addition to the currently dominant contribution from mineral‐poor surface water. Most studies observe or predict an increase in major ion, phosphate, and silicate export with this shift towards greater groundwater contributions. However, we see conflicting accounts of whether the delivery of inorganic nitrogen and organic matter will increase or decrease with warming and permafrost thaw. It is important to note that uncertainties in the predictions of the total flux of biogeochemical constituents are tightly linked to future uncertainties in discharge of rivers. Nonetheless, it is clear that over the next century there will be important shifts in the river transport of organic matter, inorganic nutrients, and major ions, which may in turn have critical implications for primary production and carbon cycling on arctic shelves and in the Arctic Ocean basin interior. Copyright © 2008 John Wiley &amp; Sons, Ltd.

Generic error model for calibration and uncertainty estimation of hydrological models

Because of the necessary simplification of the complex natural processes and the limited availability of observations, model simulations are always uncertain and this uncertainty should be quantified. In this contribution, the model error is quantified using a combined procedure. For the uncertainty of discharge due to meteorological input, a stochastic simulation method is used. To quantify the effect of process representation and parameterization, a sensitivity analysis is carried out. It is assumed that the model error due to process uncertainty is proportional to the sensitivity. The final model error variance can thus be calculated from the stochastic errors and the process sensitivities. The coefficients used for the quantification are estimated simultaneously with the model parameters. The methodology presented produces error series that are normally distributed and that represent the varying importance of different processes in time. This uncertainty time series can be used as a weighting factor to normalize the model residuals during calibration so that the assumptions of least squares optimization are fulfilled. Calibration and uncertainty estimation are demonstrated with an example application of a distributed Hydrological Bureau Waterbalance (HBV) model of three watersheds in the Neckar basin in southwest Germany. The model residual distributions are presented and compared to a standard calibration method. Further, it is shown that the new methodology leads to more realistic confidence intervals for model simulations. Although applied to the HBV model as an example, the method is general and can be applied to any model and also in conjunction with other uncertainty estimation techniques.

The importance of statistical uncertainties in selecting appropriate methods for estimation of extremes

Statistical uncertainties in the estimation of extremes are normally not well considered in present practice or even present research. This paper shows the application of extreme distributions to river variables with non negligible levels of uncertainty and presents an approach to deal with them. Four distribution types (Gumbel, Pearson III (log shifted Gamma), Lognormal and Generalised Pareto) are fitted to peaks over threshold or annual maxima data sets of river discharges. Piecewise exponential distributions are used to summarise the four distribution types and bootstrapping methods for quantifying the uncertainty in the design discharges. The paper furthermore presents relationships between probability density functions, convergence theorems and statistical tests to judge the goodness of fits.

A geostatistical approach for quantification of contaminant mass discharge uncertainty using multilevel sampler measurements

Contaminant mass discharge across a control plane downstream of a dense nonaqueous phase liquid (DNAPL) source zone has great potential to serve as a metric for the assessment of the effectiveness of source zone treatment technologies and for the development of risk‐based source‐plume remediation strategies. However, too often the uncertainty of mass discharge estimated in the field is not accounted for in the analysis. In this paper, a geostatistical approach is proposed to estimate mass discharge and to quantify its associated uncertainty using multilevel transect measurements of contaminant concentration (C) and hydraulic conductivity (K). The approach adapts thep‐field simulation algorithm to propagate and upscale the uncertainty of mass discharge from the local uncertainty models ofCandK. Application of this methodology to numerically simulated transects shows that, with a regular sampling pattern, geostatistics can provide an accurate model of uncertainty for the transects that are associated with low levels of source mass removal (i.e., transects that have a large percentage of contaminated area). For high levels of mass removal (i.e., transects with a few hot spots and large areas of near‐zero concentration), a total sampling area equivalent to 6∼7% of the transect is required to achieve accurate uncertainty modeling. A comparison of the results for different measurement supports indicates that samples taken with longer screen lengths may lead to less accurate models of mass discharge uncertainty. The quantification of mass discharge uncertainty, in the form of a probability distribution, will facilitate risk assessment associated with various remediation strategies.

Comparison of uncertainty estimates in international metrological and ecological guides

Comparisons have been made on the international ecological guidelines CORINAIR, IPCC, and the GHG Protocol, which implement evaluation of the uncertainties in pollution discharges to the atmosphere, greenhouse gases, and metrological guidelines on estimating the uncertainty of GUM measurements. The main sources of uncertainty are identified and their values are estimated together with their contribution to the general uncertainty in the estimation of greenhouse gas discharges.

Using internal catchment information to reduce the uncertainty of discharge and baseflow predictions

The semi-distributed hydrological model TOPMODEL was tested with data from the Can Vila research basin (Vallcebre) in order to verify its adequacy for simulating runoff and the relative contributions from saturated overland flow and groundwater flow. After a test of the overall performance of the model, only data from a wet period were selected for this work. The test was performed using the GLUE method. The model was conditioned on continuous discharge and water table records. Furthermore, point measurements of recession flow simultaneous with water table depth and the extent of saturated areas were used to condition the distributions of the more relevant parameters, using new or updated evaluation measures. A wide range of parameter sets provided acceptable results for flow simulation when the model was conditioned on flow data alone, and the uncertainty of prediction of the contribution from groundwater was extremely large. However, conditioning on water table records and the distribution of parameters obtained from point observations strongly reduced the uncertainty of predictions for both stream flow and groundwater contribution.

Hydraulic model calibration for extreme floods in bedrock‐confined channels: case study from northern Thailand

AbstractPalaeoflood reconstructions based on stage evidence are typically conducted in data‐poor field settings. Few opportunities exist to calibrate the hydraulic models used to estimate discharge from this evidence. Consequently, an important hydraulic model parameter, the roughness coefficient (e.g. Manning's n), is typically estimated by a range of approximate techniques, such as ‘visual estimation’ and semi‐empirical equations. These techniques contribute uncertainty to resulting discharge estimates, especially where the study reach exhibits sensitivity in the discharge–Manning's n relation. We study this uncertainty within a hydraulic model for a large flood of known discharge on the Mae Chaem River, northern Thailand. Comparison of the ‘calibrated’ Manning's n with that obtained from semi‐empirical equations indicates that these underestimate roughness. Substantial roughness elements in the extra‐channel zone, inundated during large events, contribute significant additional sources of flow resistance that are captured neither by the semi‐empirical equations, nor by existing models predicting stage–roughness variations. This bedrock channel exhibits a complex discharge–Manning's n relation, and reliable estimates of the former are dependent upon realistic assignment of the latter. Our study demonstrates that a large recent flood can provide a valuable opportunity to constrain this parameter, and this is illustrated when we model a palaeoflood event in the same reach, and subsequently examine the magnitude–return period consequences of discharge uncertainty within a flood frequency analysis, which contributes its own source of uncertainty. Copyright © 2005 John Wiley &amp; Sons, Ltd.

Cold region river discharge uncertainty—estimates from large Russian rivers

We develop an error model to understand the reliability and accuracy of river discharge datasets that are now being used for a variety of important global change questions. The developed error model for cold region river discharge uses standard hydrometric data along with information on the frequency and precision of measurements, characteristics of river channel capacity, and method of discharge computation. The uncertainties of daily, monthly and annual discharge data for the downstream gauges of the six largest Eurasian rivers (Severnaya Dvina, Pechora, Ob', Yenisei, Lena and Kolyma) in the pan-Arctic drainage along with uncertainty of aggregated annual time series are evaluated using the suggested methodology. The study shows that uncertainties associated with discharge determination significantly change from year to year and strongly depend on the computational methods used and frequency of discharge measurements. Recent work by Peterson et al. (2002) has shown increases in river discharge to the Arctic Ocean of the six largest Eurasian rivers of 7% from 1936 to 1999. This paper focuses on determination of reliability in the discharge data which provided such conclusion. The obtained results further confirm the findings of Peterson et al. (2002) concerning the rise in river discharge. We found that errors of the total annual discharge for the six rivers over the period 1950–2000 are in the range 1.5–3.5%. The long-term trend of the observed discharge from these six rivers into the Arctic Ocean for 1936–2000, along with uncertainty associated with discharge data, is 2.0±0.4 km 3/year.

Stochastic Modelling of the Impact of Flood Protection Measures Along the River Waal in the Netherlands

River flooding is a problem of international interest. In the past few years many countries suffered from severe floods. A large part of the Netherlands is below sea level and river levels. The Dutch flood defences along the river Rhine are designed for water levels with a probability of exceedance of 1/1250 per year. These water levels are computed with a hydrodynamic model using a deterministic bed level and a deterministic design discharge. Traditionally, the safety against flooding in the Netherlands is obtained by building and reinforcing dikes. Recently, a new policy was proposed to cope with increasing design discharges in the Rhine and Meuse rivers. This policy is known as the Room for the River (RfR) policy, in which a reduction of flood levels is achieved by measures creating space for the river, such as dike replacement, side channels and floodplain lowering. As compared with dike reinforcement, these measures may have a stronger impact on flow and sediment transport fields, probably leading to stronger morphological effects. As a result of the latter the flood conveyance capacity may decrease over time. An a priori judgement of safety against flooding on the basis of an increased conveyance capacity of the river can be quite misleading. Therefore, the determination of design water levels using a fixed-bed hydrodynamic model may not be justified and the use of a mobile-bed approach may be more appropriate. This problem is addressed in this paper, using a case study of the river Waal (one of the Rhine branches in the Netherlands). The morphological response of the river Waal to a flood protection measure (floodplain lowering in combination with summer levee removal) is analysed. The effect of this measure is subject to various sources of uncertainty. Monte Carlo simulations are applied to calculate the impact of uncertainties in the river discharge on the bed levels. The impact of the “uncertain” morphological response on design flood level predictions is analysed for three phenomena, viz. the impact of the spatial morphological variation over years, the impact of the seasonal morphological variation and the impact of the morphological variability around bifurcation points. The impact of seasonal morphological variations turns out to be negligible, but the other two phenomena appear to have each an appreciable impact (order of magnitude 0.05–0.1 m) on the computed design water levels. We have to note however, that other sources of uncertainty (e.g. uncertainty in hydraulic roughness predictor), which may be of influence, are not taken into consideration. In fact, the present investigation is limited to the sensitivity of the design water levels to uncertainties in the predicted bed level.