Does the complexity in temporal precipitation disaggregation matter for a lumped hydrological model?

ABSTRACT

Flood peaks and volumes are essential design variables and can be simulated by precipitation–runoff (P–R) modelling. The high-resolution precipitation time series that are often required for this purpose can be generated by various temporal disaggregation methods. Here, we compare a simple method (M1, one parameter), focusing on the effective precipitation duration for flood simulations, with a multiplicative cascade model (M2, 32/36 parameters). While M2 aims at generating realistic characteristics of precipitation time series, M1 aims only at accurately reproducing flood variables by P–R modelling. Both disaggregation methods were tested on precipitation time series of nine Swiss mesoscale catchments. The generated high-resolution time series served as input for P–R modelling using a lumped HBV model. The results indicate that differences identified in precipitation characteristics of disaggregated time series vanish when introduced into the lumped hydrological model. Moreover, flood peaks were more sensitive than flood volumes to the choice of disaggregation method.     

Sensitivity of a hydraulic model to channel erosion uncertainty during extreme flooding

Recent research into flood modelling has primarily concentrated on the simulation of inundation flow without considering the influences of channel morphology. River channels are often represented by a simplified geometry that is implicitly assumed to remain unchanged during flood simulations. However, field evidence demonstrates that significant morphological changes can occur during floods to mobilize the boundary sediments. Despite this, the effect of channel morphology on model results has been largely unexplored. To address this issue, the impact of channel cross‐section geometry and channel long‐profile variability on flood dynamics is examined using an ensemble of a 1D–2D hydraulic model (LISFLOOD‐FP) of the ~1 : 2000 year recurrence interval floods in Cockermouth, UK, within an uncertainty framework. A series of simulated scenarios of channel erosional changes were constructed on the basis of a simple velocity‐based model of critical entrainment. A Monte‐Carlo simulation framework was used to quantify the effects of this channel morphology together with variations in the channel and floodplain roughness coefficients, grain size characteristics and critical shear stress on measures of flood inundation. The results showed that the bed elevation modifications generated by the simplistic equations reflected an approximation of the observed patterns of spatial erosion that enveloped observed erosion depths. The effect of uncertainty on channel long‐profile variability only affected the local flood dynamics and did not significantly affect the friction sensitivity and flood inundation mapping. The results imply that hydraulic models generally do not need to account for within event morphodynamic changes of the type and magnitude of event modelled, as these have a negligible impact that is smaller than other uncertainties, e.g. boundary conditions. Instead, morphodynamic change needs to happen over a series of events to become large enough to change the hydrodynamics of floods in supply limited gravel‐bed rivers such as the one used in this research. Copyright © 2014 John Wiley & Sons, Ltd.     

Probabilistic modelling of flood events using the entropy copula

The estimation of flood frequency is vital for the flood control strategies and hydraulic structure design. Generating synthetic flood events according to statistical properties of observations is one of plausible methods to analyze the flood frequency. Due to the statistical dependence among the flood event variables (i.e. the flood peak, volume and duration), a multidimensional joint probability estimation is required. Recently, the copula method is widely used for multivariable dependent structure construction, however, the copula family should be chosen before application and the choice process is sometimes rather subjective. The entropy copula, a new copula family, employed in this research proposed a way to avoid the relatively subjective process by combining the theories of copula and entropy. The analysis shows the effectiveness of the entropy copula for probabilistic modelling the flood events of two hydrological gauges, and a comparison of accuracy with the popular copulas was made. The Gibbs sampling technique was applied for trivariate flood events simulation in order to mitigate the calculation difficulties of extending to three dimension directly. The simulation results indicate that the entropy copula is a simple and effective copula family for trivariate flood simulation.     

Appropriate temporal resolution of precipitation data for discharge modelling in pre-alpine catchments

Precipitation time series with high temporal resolution are desired for hydrological modelling and flood studies. Yet the choice of an appropriate resolution is not straightforward because the use of too high a temporal resolution increases the data requirements, computational costs and, presumably, associated uncertainty, while performance improvement may be indiscernible. In this study, the effect of averaging hourly precipitation on model performance and associated uncertainty is investigated using two data sources: station network precipitation (SNP) and radar-based precipitation (RBP). From these datasets, time series of different temporal resolutions were generated, and runoff was simulated for 13 pre-alpine catchments with a bucket-type model. Our results revealed that different temporal resolutions were required for an acceptable model performance depending on the catchment size and data source. These were 1–12 h for small (16–59 km²), 3-21 h for medium (60–200 km²), and 24 h for large (200–939 km²) catchments.     

Check dam layout optimization on the stream network for flood mitigation: surrogate modelling with uncertainty handling

The configuration of check dams and their numbers throughout a basin are important factors for reducing floods in downstream reaches of rivers. In this paper, a stochastic model based on surrogate modelling and Monte Carlo simulation, linked to an evolutionary optimization tool, is developed to assign the optimal sites and number of check dams on a stream network. To handle uncertainty of rainfall variables and their correlation structures, the copula method is employed and an artificial neural network (ANN) is used to emulate the computationally expensive hydrological model, HEC-HMS, within the optimization routines. The prepared modelling framework is applied to a mountainous basin to determine the arrangement of check dams in its sub-basins. The experimental results show that optimal strategies can reduce the expected value of peak flood discharges by up to 50%, with significantly lower costs or number of check dams, relative to a traditional approach with a large number of check dams in sub-basins, presenting a maximum of 21% efficiency.     

A regional Bayesian POT model for flood frequency analysis

Flood frequency analysis is usually based on the fitting of an extreme value distribution to the local streamflow series. However, when the local data series is short, frequency analysis results become unreliable. Regional frequency analysis is a convenient way to reduce the estimation uncertainty. In this work, we propose a regional Bayesian model for short record length sites. This model is less restrictive than the index flood model while preserving the formalism of “homogeneous regions”. The performance of the proposed model is assessed on a set of gauging stations in France. The accuracy of quantile estimates as a function of the degree of homogeneity of the pooling group is also analysed. The results indicate that the regional Bayesian model outperforms the index flood model and local estimators. Furthermore, it seems that working with relatively large and homogeneous regions may lead to more accurate results than working with smaller and highly homogeneous regions.     

Driving a lumped hydrological model with precipitation output from weather generators of different complexity

ABSTRACT

This paper deals with the question of whether a lumped hydrological model driven with lumped daily precipitation time series from a univariate single-site weather generator can produce equally good results compared to using a multivariate multi-site weather generator, where synthetic precipitation is first generated at multiple sites and subsequently lumped. Three different weather generators were tested: a univariate “Richardson type” model, an adapted univariate Richardson type model with an improved reproduction of the autocorrelation of precipitation amounts and a semi-parametric multi-site weather generator. The three modelling systems were evaluated in two Alpine study areas by comparing the hydrological output with respect to monthly and daily statistics as well as extreme design flows. The application of a univariate Richardson type weather generator to lumped precipitation time series requires additional attention. Established parametric distribution functions for single-site precipitation turned out to be unsuitable for lumped precipitation time series and led to a large bias in the hydrological simulations. Combining a multi-site weather generator with a hydrological model produced the least bias.     

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.     

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.     

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

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

Copula‐based uncertainty modelling: application to multisensor precipitation estimates

The multisensor precipitation estimates (MPE) data, available in hourly temporal and 4 km × 4 km spatial resolution, are produced by the National Weather Service and mosaicked as a national product known as Stage IV. The MPE products have a significant advantage over rain gauge measurements due to their ability to capture spatial variability of rainfall. However, the advantages are limited by complications related to the indirect nature of remotely sensed precipitation estimates. Previous studies confirm that efforts are required to determine the accuracy of MPE and their associated uncertainties for future use in hydrological and climate studies. So far, various approaches and extensive research have been undertaken to develop an uncertainty model. In this paper, an ensemble generator is presented for MPE products that can be used to evaluate the uncertainty of rainfall estimates. Two different elliptical copula families, namely, Gaussian and t‐copula are used for simulations. The results indicate that using t‐copula may have significant advantages over the well‐known Gaussian copula particularly with respect to extremes. Overall, the model in which t‐copula was used for simulation successfully generated rainfall ensembles with similar characteristics to those of the ground reference measurements. Copyright © 2010 John Wiley & Sons, Ltd.     

Validation of a full hydrodynamic model for large‐scale hydrologic modelling in the Amazon

A key aspect of large river basins partially neglected in large‐scale hydrological models is river hydrodynamics. Large‐scale hydrologic models normally simulate river hydrodynamics using simplified models that do not represent aspects such as backwater effects and flood inundation, key factors for some of the largest rivers of the world, such as the Amazon. In a previous paper, we have described a large‐scale hydrodynamic approach resultant from an improvement of the MGB‐IPH hydrological model. It uses full Saint Venant equations, a simple storage model for flood inundation and GIS‐based algorithms to extract model parameters from digital elevation models. In the present paper, we evaluate this model in the Solimões River basin. Discharge results were validated using 18 stream gauges showing that the model is accurate. It represents the large delay and attenuation of flood waves in the Solimões basin, while simplified models, represented here by Muskingum Cunge, provide hydrographs are wrongly noisy and in advance. Validation against 35 stream gauges shows that the model is able to simulate observed water levels with accuracy, representing their amplitude of variation and timing. The model performs better in large rivers, and errors concentrate in small rivers possibly due to uncertainty in river geometry. The validation of flood extent results using remote sensing estimates also shows that the model accuracy is comparable to other flood inundation modelling studies. Results show that (i) river‐floodplain water exchange and storage, and (ii) backwater effects play an important role for the Amazon River basin hydrodynamics. Copyright © 2011 John Wiley & Sons, Ltd.     

Application of Gibbs’ model to urban drainage networks: a case study in southwestern Chicago,USA

Hydrologic analysis of urban drainage networks often encounters a number of issues, including data acquisition and preparation for modelling, which can be costly and time‐consuming processes. Moreover, it can get more challenging with missing data and complex loops inside networks. In this article, Gibbs’ model is applied to urban drainage networks to investigate the possibility of replacing an actual existing urban drainage network in terms of the shape and peak flow of the hydrographs at the outlet. The characteristic network configuration is given as a value of a parameter β of Gibbs’ model. Instead of the actual network, stochastic networks from Monte‐Carlo simulation are utilized to obtain a synthetic width function from the generated networks, and runoff hydrographs are estimated based on it. The results show that the synthetic width function and the resulting hydrographs obtained from the networks simulated by Gibbs’ model are close to those from the actual network. The result also shows that even the behaviour of a looped network can be approximated by equivalent dendritic networks generated by Gibbs’ model. The applicability of a stochastic network model in urban catchment implies a complement to modelling approaches in case of data unavailability. Moreover, the network property (β) is utilized not only to estimate the discharge hydrograph of a catchment but also as a key link to evaluate the effect from rainstorm movement in urban catchments. Copyright © 2012 John Wiley & Sons, Ltd.     

Incorporating probabilistic approach into local multi-criteria decision analysis for flood susceptibility assessment

Flood risk management can be enhanced by integrating geographic information system (GIS) with multi-criteria decision analysis (MCDA). However, the conventional, deterministic MCDA methods ignore uncertainty in the decision-making process and fail to account for local variability in criteria values and preferences. Therefore, a spatially explicit MCDA model which effectively incorporates spatial heterogeneity is required. In this paper, a probabilistic or stochastic MCDA method which incorporates the uncertainty into a local weighted linear combination (WLC) was utilized to evaluate flood susceptibility; and an application case in Gucheng County, Central China, was developed. A GIS database of geomorphological and hydro-meteorological criteria contributing to flood susceptibility analysis was constructed using six conditioning factors: digital elevation model (DEM), slope (SL), maximum three-day precipitation (M3DP), topographic wetness index (TWI), distance from the river (DR), and Soil Conservation Service Curve Number (SCS-CN). The results of local WLC were compared with those of the global WLC. It shows that the local WLC model can provide much more valuable information about the spatial patterns of criterion values, ranges, weights, trade-offs and overall scores, whereas the global WLC can only depict the spatial distribution of criterion values and overall scores. The local WLC can also help to prioritize the most susceptible locations within a neighborhood when navigating the disaster assistance process. Moreover, the uncertainty analysis of criteria weights increases the degree of confidence in the model output. It is concluded that the presented approach can provide more insights and understanding of the nature of the flood susceptibility than global WLC.     

The integrated effects of climate and hydrologic uncertainty on future flood risk assessments

This work examines future flood risk within the context of integrated climate and hydrologic modelling uncertainty. The research questions investigated are (1) whether hydrologic uncertainties are a significant source of uncertainty relative to other sources such as climate variability and change and (2) whether a statistical characterization of uncertainty from a lumped, conceptual hydrologic model is sufficient to account for hydrologic uncertainties in the modelling process. To investigate these questions, an ensemble of climate simulations are propagated through hydrologic models and then through a reservoir simulation model to delimit the range of flood protection under a wide array of climate conditions. Uncertainty in mean climate changes and internal climate variability are framed using a risk‐based methodology and are explored using a stochastic weather generator. To account for hydrologic uncertainty, two hydrologic models are considered, a conceptual, lumped parameter model and a distributed, physically based model. In the conceptual model, parameter and residual error uncertainties are quantified and propagated through the analysis using a Bayesian modelling framework. The approach is demonstrated in a case study for the Coralville Dam on the Iowa River, where recent, intense flooding has raised questions about potential impacts of climate change on flood protection adequacy. Results indicate that the uncertainty surrounding future flood risk from hydrologic modelling and internal climate variability can be of the same order of magnitude as climate change. Furthermore, statistical uncertainty in the conceptual hydrological model can capture the primary structural differences that emerge in flood damage estimates between the two hydrologic models. Copyright © 2014 John Wiley & Sons, Ltd.     

The influence of hydroclimatic variability on flood frequency in the Lower Rhine

Climate change is expected to significantly affect flooding regimes of river systems in the future. For Western Europe, flood risk assessments generally assume an increase in extreme events and flood risk, and as a result major investments are planned to reduce their impacts. However, flood risk assessments for the present day and the near future suffer from uncertainty, coming from short measurements series, limited precision of input data, arbitrary choices for particular statistical and modelling approaches, and climatic non‐stationarities. This study demonstrates how historical and sedimentary information can extend data records, adds important information on extremes, and generally improves flood risk assessments. The collection of specific data on the occurrence and magnitude of extremes and the natural variability of the floods is shown to be of paramount importance to reduce uncertainty in our understanding of flooding regime changes in a changing climate. For the Lower Rhine (the Netherlands and Germany) estimated recurrence times and peak discharges associated with the current protection levels correlate poorly with historical and sedimentary information and seem biased towards the recent multi‐decadal period of increased flood activity. Multi‐decadal and centennial variability in flood activity is recorded in extended series of discharge data, historical information and sedimentary records. Over the last six centuries that variability correlates with components of the Atlantic climate system such as the North Atlantic Oscillation (NAO) and Atlantic Multi‐decadal Oscillation (AMO). These climatic non‐stationarities importantly influence flood activity and the outcomes of flood risk assessments based on relatively short measurement series. Copyright © 2016 John Wiley & Sons, Ltd.     

A comparison of different methods for speleothem age modelling

Speleothems, such as stalagmites and flowstones, can be dated with unprecedented precision in the range of the last 650,000 a by the ²³⁰Th/U-method, which is considered as one of their major advantages as climate archives. However, a standard approach for the construction of speleothem age models and the estimation of the corresponding uncertainty has not been established yet.Here we apply five age modelling approaches (StalAge, OxCal, a finite positive growth rate model and two spline-based models) to a synthetic speleothem growth model and two natural samples. All data sets contain problematic features such as outliers, age inversions, large and abrupt changes in growth rate as well as hiatuses.For data sets constrained by a large number of ages and not including problematic sections, all age models provide similar results. In case of problematic sections, the algorithms provide significantly different age models and uncertainty ranges.StalAge, OxCal and the finite positive growth rate model are, in general, more flexible since they are capable of modelling hiatuses and account for problematic sections by increased uncertainty. The spline-based age models, in contrast, reveal problems in modelling problematic sections.Application to the synthetic data set allows testing the performance of the algorithms because the ‘true’ age model is available and can be compared with the age models. OxCal and StalAge generally show a good performance for this example, even if they are inaccurate for a short section in the area of a hiatus. The two spline-based models and the finite positive growth rate model show larger inaccurately modelled sections.     

Modeling short duration extreme precipitation patterns using copula and generalized maximum pseudo-likelihood estimation with censoring

The paper aims to develop researches on the spatial variability of heavy rainfall events estimation using spatial copula analysis. To demonstrate the methodology, short time resolution rainfall time series from Stuttgart region are analyzed. They are constituted by rainfall observations on continuous 30 min time scale recorded over a network composed by 17 raingages for the period July 1989–July 2004. The analysis is performed aggregating the observations from 30 min up to 24 h. Two parametric bivariate extreme copula models, the Husler–Reiss model and the Gumbel model are investigated. Both involve a single parameter to be estimated. Thus, model fitting is operated for every pair of stations for a giving time resolution. A rainfall threshold value representing a fixed rainfall quantile is adopted for model inference. Generalized maximum pseudo-likelihood estimation is adopted with censoring by analogy with methods of univariate estimation combining historical and paleoflood information with systematic data. Only pairs of observations greater than the threshold are assumed as systematic data. Using the estimated copula parameter, a synthetic copula field is randomly generated and helps evaluating model adequacy which is achieved using Kolmogorov Smirnov distance test. In order to assess dependence or independence in the upper tail, the extremal coefficient which characterises the tail of the joint bivariate distribution is adopted. Hence, the extremal coefficient is reported as a function of the interdistance between stations. If it is less than 1.7, stations are interpreted as dependent in the extremes. The analysis of the fitted extremal coefficients with respect to stations inter distance highlights two regimes with different dependence structures: a short spatial extent regime linked to short duration intervals (from 30 min to 6 h) with an extent of about 8 km and a large spatial extent regime related to longer rainfall intervals (from 12 h to 24 h) with an extent of 34 to 38 km.     

Estimation of flood inundation probabilities using global hazard indexes based on hydrodynamic variables

In this paper a new procedure to derive flood hazard maps incorporating uncertainty concepts is presented. The layout of the procedure can be resumed as follows: (1) stochastic input of flood hydrograph modelled through a direct Monte-Carlo simulation based on flood recorded data. Generation of flood peaks and flow volumes has been obtained via copulas, which describe and model the correlation between these two variables independently of the marginal laws involved. The shape of hydrograph has been generated on the basis of a historical significant flood events, via cluster analysis; (2) modelling of flood propagation using a hyperbolic finite element model based on the DSV equations; (3) definition of global hazard indexes based on hydro-dynamic variables (i.e., water depth and flow velocities). The GLUE methodology has been applied in order to account for parameter uncertainty. The procedure has been tested on a flood prone area located in the southern part of Sicily, Italy. Three hazard maps have been obtained and then compared.