Hydrological Sciences Journal Contents 2002

Abstract

Flood frequency estimation is crucial in both engineering practice and hydrological research. Regional analysis of flood peak discharges is used for more accurate estimates of flood quantiles in ungauged or poorly gauged catchments. This is based on the identification of homogeneous zones, where the probability distribution of annual maximum peak flows is invariant, except for a scale factor represented by an index flood. The numerous applications of this method have highlighted obtaining accurate estimates of index flood as a critical step, especially in ungauged or poorly gauged sections, where direct estimation by sample mean of annual flood series (AFS) is not possible, or inaccurate. Therein indirect methods have to be used. Most indirect methods are based upon empirical relationships that link index flood to hydrological, climatological and morphological catchment characteristics, developed by means of multi-regression analysis, or simplified lumped representation of rainfall–runoff processes. The limits of these approaches are increasingly evident as the size and spatial variability of the catchment increases. In these cases, the use of a spatially-distributed, physically-based hydrological model, and time continuous simulation of discharge can improve estimation of the index flood. This work presents an application of the FEST-WB model for the reconstruction of 29 years of hourly streamflows for an Alpine snow-fed catchment in northern Italy, to be used for index flood estimation. To extend the length of the simulated discharge time series, meteorological forcings given by daily precipitation and temperature at ground automatic weather stations are disaggregated hourly, and then fed to FEST-WB. The accuracy of the method in estimating index flood depending upon length of the simulated series is discussed, and suggestions for use of the methodology provided.

Editor D. Koutsoyiannis     

The formation of groups for regional flood frequency analysis

Abstract

A new technique is developed for identifying groups for regional flood frequency analysis. The technique uses a clustering algorithm as a starting point for partitioning the collection of catchments. The groups formed using the clustering algorithm are subsequently revised to improve the regional characteristics based on three requirements that are defined for effective groups. The result is overlapping groups that can be used to estimate extreme flow quantiles for gauged or ungauged catchments. The technique is applied to a collection of catchments from India and the results indicate that regions with the desired characteristics can be identified using the technique. The use of the groups for estimating extreme flow quantiles is demonstrated for three example sites.     

Derivation and verification of empirical catchment response time equations for medium to large catchments in South Africa

Despite uncertainties and errors in measurement, observed peak discharges are the best estimate of the true peak discharge from a catchment. However, in ungauged catchments, the catchment response time is a fundamental input to all methods of estimating peak discharges; hence, errors in estimated catchment response time directly impact on estimated peak discharges. In South Africa, this is particularly the case in ungauged medium to large catchments where practitioners are limited to use empirical methods that were calibrated on small catchments not located in South Africa. The time to peak (T_P), time of concentration (T_C) and lag time (T_L) are internationally the most frequently used catchment response time parameters and are normally estimated using either hydraulic or empirical methods. Almost 95% of all the time parameter estimation methods developed internationally are empirically based. This paper presents the derivation and verification of empirical T_P equations in a pilot scale study using 74 catchments located in four climatologically different regions of South Africa, with catchment areas ranging from 20 km² to 35 000 km². The objective is to develop unique relationships between observed T_P values and key climatological and geomorphological catchment predictor variables in order to estimate catchment T_P values at ungauged catchments. The results show that the derived empirical T_P equation(s) meet the requirement of consistency and ease of application. Independent verification tests confirmed the consistency, while the statistically significant independent predictor variables included in the regressions provide a good estimation of catchment response times and are also easy to determine by practitioners when required for future applications in ungauged catchments. It is recommended that the methodology used in this study should be expanded to other catchments to enable the development of a regional approach to improve estimation of time parameters on a national‐scale. However, such a national‐scale application would not only increase the confidence in using the suggested methodology and equation(s) in South Africa, but also highlights that a similar approach could be adopted internationally. Copyright © 2016 John Wiley & Sons, Ltd.     

György Kovacs 1925–1988

Abstract

There has been a trend in recent years towards the development and popularity of physically-based deterministic models. However, the application of such models is not without difficulties. This paper investigates the usefulness of a conceptual single-event model for simulating floods from catchments covering a wide variety of climatic and physiographic areas. The model has been calibrated on a group of catchments and the calibrated parameter values related to physical catchment indices. The resulting quantitative relationships are assessed with respect to their value for estimating the parameter values of the model when calibration is not possible. The results indicate that the technique is likely to provide flood estimations for medium sized catchments (5–150 km²) that are more reliable than several flood estimation methods currently in use in South Africa.     

Application of artificial neural networks in regional flood frequency analysis: a case study for Australia

Regional flood frequency analysis (RFFA) is widely used in practice to estimate flood quantiles in ungauged catchments. Most commonly adopted RFFA methods such as quantile regression technique (QRT) assume a log-linear relationship between the dependent and a set of predictor variables. As non-linear models and universal approximators, artificial neural networks (ANN) have been widely adopted in rainfall runoff modeling and hydrologic forecasting, but there have been relatively few studies involving the application of ANN to RFFA for estimating flood quantiles in ungauged catchments. This paper thus focuses on the development and testing of an ANN-based RFFA model using an extensive Australian database consisting of 452 gauged catchments. Based on an independent testing, it has been found that ANN-based RFFA model with only two predictor variables can provide flood quantile estimates that are more accurate than the traditional QRT. Seven different regions have been compared with the ANN-based RFFA model and it has been shown that when the data from all the eastern Australian states are combined together to form a single region, the ANN presents the best performing RFFA model. This indicates that a relatively larger dataset is better suited for successful training and testing of the ANN-based RFFA models.     

Machine learning based identification of dominant controls on runoff dynamics

Hydrological models used for flood prediction in ungauged catchments are commonly fitted to regionally transferred data. The key issue of this procedure is to identify hydrologically similar catchments. Therefore, the dominant controls for the process of interest have to be known. In this study, we applied a new machine learning based approach to identify the catchment characteristics that can be used to identify the active processes controlling runoff dynamics. A random forest (RF) regressor has been trained to estimate the drainage velocity parameters of a geomorphologic instantaneous unit hydrograph (GIUH) in ungauged catchments, based on regionally available data. We analyzed the learning procedure of the algorithm and identified preferred donor catchments for each ungauged catchment. Based on the obtained machine learning results from catchment grouping, a classification scheme for drainage network characteristics has been derived. This classification scheme has been applied in a flood forecasting case study. The results demonstrate that the RF could be trained properly with the selected donor catchments to successfully estimate the required GIUH parameters. Moreover, our results showed that drainage network characteristics can be used to identify the influence of geomorphological dispersion on the dynamics of catchment response.     

Synthetic design hydrographs for ungauged catchments: a comparison of regionalization methods

Design flood estimates for a given return period are required in both gauged and ungauged catchments for hydraulic design and risk assessments. Contrary to classical design estimates, synthetic design hydrographs provide not only information on the peak magnitude of events but also on the corresponding hydrograph volumes together with the hydrograph shapes. In this study, we tested different regionalization approaches to transfer parameters of synthetic design hydrographs from gauged to ungauged catchments. These approaches include classical regionalization methods such as linear regression techniques, spatial methods, and methods based on the formation of homogeneous regions. In addition to these classical approaches, we tested nonlinear regression models not commonly used in hydrological regionalization studies, such as random forest, bagging, and boosting. We found that parameters related to the magnitude of the design event can be regionalized well using both linear and nonlinear regression techniques using catchment area, length of the main channel, maximum precipitation intensity, and relief energy as explanatory variables. The hydrograph shape, however, was found to be more difficult to regionalize due to its high variability within a catchment. Such variability might be better represented by looking at flood-type specific synthetic design hydrographs.     

Use of a parsimonious rainfall–run‐off model for predicting hydrological response in ungauged basins

A new parameter parsimonious rainfall–run‐off model, the Distance Distribution Dynamics (DDD) model, is used to simulate hydrological time series at ungauged sites in the Lygne basin in Norway. The model parameters were estimated as functions of catchment characteristics determined by geographical information system. The multiple regression equations relating catchment characteristics and model parameters were trained from 84 calibrated catchments located all over Norway, and all model parameters showed significant correlations with catchment characteristics. The significant correlation coefficients (with p‐value < 0.05) ranged from 0.22 to 0.55. The suitability of DDD for predictions in ungauged basins was tested for 17 catchments not used to estimate the multiple regression equations. For ten of the 17 catchments, deviations in Nash–Sutcliffe efficiency (NSE) criteria between the calibrated and regionalised model were less than 0.1, and for two calibrated catchments within the Lygne basin, the deviations were less than 0.08. The median NSE for the regionalized DDD for the 17 catchments for two time series was 0.66 and 0.72. Deviations in NSE between calibrated and regionalised models are well explained by the deviations between calibrated and regressed parameters describing spatial snow distribution and snowmelt respectively. The quality of the simulated run‐off series for the ungauged sites in the Lygne basin was assessed by comparing flow indices describing high, medium and low flow estimated from observed run‐off at the 17 catchments and for the simulated run‐off series. The indices estimated for the simulated series were generally well within the ranges defined by the 17 observed series. Copyright © 2014 John Wiley & Sons, Ltd.     

Indirect estimation of ungauged peak discharges in a bedrock channel with reference to design discharge selection

Geomorphological evidence and recent trash lines were used as stage indicators in a step-backwater computer model of high discharges through an ungauged bedrock channel. The simulation indicated that the peak discharge from the 26.7 m² catchment was close to 150m³s^?1 during the passage of Hurricane Charlie in August 1986. This estimate can be compared with an estimate of 130–160 m³s^?1 obtained using the Flood Studies Report (FSR) unit hydrograph methodology. Other palaeostage marks indicate that higher stages have occurred at an earlier time associated with a discharge of 200 m³s^?1. However, consideration of both the geometry of a plunge pool and transport criteria for bedrock blocks in the channel indicates that floods since 1986 have not exceeded 150 m³s^?1. Given that the estimated probable maximum flood (PMF) calculated from revised FSR procedure is at least 240 m³s^?1, it is concluded that compelling evidence for floods equal to the PMF is lacking. Taking into consideration the uncertainty of the discharge estimation, the 1986 flood computed using field evidence has a minimum return period of 100 years using the FSR procedure. This may be compared with a return period for the same event in the neighbouring gauged River Greta of > 100 years and a rainfall return period of 190 years. In as much as discharges of similar order to FSR estimates are indicated, it is concluded (a) that regional geomorphological evidence and flood simulation within ungauged catchments may be useful as a verification for hydrological estimates of recent widespread flood magnitude and (b) that palaeohydraulic computation can be useful in determining the magnitude of the local maximum [historic] flood when determining design discharges for hydraulic structures within specific catchments.     

Estimation of peak streamflows through channel geometry / Estimation de pics de débit fluviatiles à l'aide de la géométrie des cours d'eau

Abstract

Knowledge of peak discharge is essential for safe and economical planning and design of hydraulic structures. In India, as in most developing countries, the majority of river basins are either sparsely gauged or not gauged at all. The gauged records are also of short length (generally 15–30 years), therefore development of robust models is necessary for estimation of streamflows. Various studies reveal that flood estimation through channel geometry is an alternative method for ungauged catchments. It is appropriate for use where flow characteristics are poorly related to catchment area and other catchment characteristics. In the present study, stream geometry parameters for 42 river sites in central-south India were used; calibration equations were developed with data for 35 stations and tested on data for the remaining seven stations. The relationships developed between mean discharge and channel geometry parameters provide an alternative technique for estimation of mean annual channel discharge.     

Assessment of spatial transferability of process‐based hydrological model parameters in two neighbouring catchments in the Himalayan Region

Estimating the hydrological regime of ungauged catchments in the Himalayan region is challenging due to a lack of sufficient monitoring stations. In this paper, the spatial transferability of the model parameters of the process‐oriented J2000 hydrological model was investigated in 2 glaciated subcatchments of the Koshi river basin in eastern Nepal. The catchments have a high degree of similarity with respect to their static landscape features. The model was first calibrated (1986–1991) and validated (1992–1997) in the Dudh Koshi subcatchment. The calibrated and validated model parameters were then transferred to the nearby Tamor catchment (2001–2009). Sensitivity and uncertainty analyses were carried out for both subcatchments to discover the sensitivity range of the parameters in the two catchments. The model represented the overall hydrograph well in both subcatchments, including baseflow, rising and falling limbs; however, the peak flows were underestimated. The efficiency results according to both Nash–Sutcliffe (ENS) and the coefficient of determination (r²) were above 0.84 in both catchments (1986–1997 in Dudh Koshi and 2001–2009 in Tamor). The ranking of the parameters in respect to their sensitivity matched well for both catchments while taking ENS and log Nash–Sutcliffe (LNS) efficiencies into account. However, there were some differences in sensitivity to ENS and LNS for moderately and less‐sensitive parameters, although the majority (13 out of 16 for ENS and 16 out of 16 for LNS) had a sensitivity response in a similar range. The generalized uncertainty likelihood estimation results suggest that the parameter uncertainty are most of the time within the range and the ensemble mean matches very good (ENS: 0.84) with observed discharge. The results indicate that transfer of the J2000 parameters to a neighbouring catchment in the Himalayan region with similar physiographic landscape characteristics is viable. This indicates the possibility of applying a calibrated process‐based J2000 model to other ungauged catchments in the Himalayan region, which could provide important insights into the hydrological system dynamics and provide much needed information to support water resources planning and management.     

Derivation of a curve number and kinematic-wave based flood frequency distribution

Abstract

The physically-based flood frequency models use readily available rainfall data and catchment characteristics to derive the flood frequency distribution. In the present study, a new physically-based flood frequency distribution has been developed. This model uses bivariate exponential distribution for rainfall intensity and duration, and the Soil Conservation Service-Curve Number (SCS-CN) method for deriving the probability density function (pdf) of effective rainfall. The effective rainfall-runoff model is based on kinematic-wave theory. The results of application of this derived model to three Indian basins indicate that the model is a useful alternative for estimating flood flow quantiles at ungauged sites.     

Estimating flash flood discharge in an ungauged mountain catchment with 2D hydraulic models and dendrogeomorphic palaeostage indicators

There is still wide uncertainty about past flash‐flood processes in mountain regions owing to the lack of systematic databases on former events. This paper presents a methodology to reconstruct peak discharge of flash floods and illustrates a case in an ungauged catchment in the Spanish Central System. The use of dendrogeomorphic evidence (i.e. scars on trees) together with the combined use of a two‐dimensional (2D) numerical hydraulic model and a terrestrial laser scan (TLS) has allowed estimation of peak discharge of a recent flash flood. The size and height distribution of scars observed in the field have been used to define three hypothetical scenarios (S_min or minimum scenario; S_med or medium scenario; and S_max or maximum scenario), thus illustrating the uncertainty involved in peak‐discharge estimation of flash floods in ungauged torrents. All scars analysed with dendrogeomorphic techniques stem from a large flash flood which took place on 17 December 1997. On the basis of the scenarios, peak discharge is estimated to 79 ± 14 m³ s^?1. The average deviation obtained between flood stage and expected scar height was ? 0·09 ± 0·53 m. From the data, it becomes obvious that the geomorphic position of trees is the main factor controlling deviation rate. In this sense, scars with minimum deviation were located on trees growing in exposed locations, especially on unruffled bedrock where the model predicts higher specific kinetic energy. The approach used in this study demonstrates the potential of tree‐ring analysis in palaeohydrology and for flood‐risk assessment in catchments with vulnerable goods and infrastructure. Copyright © 2010 John Wiley & Sons, Ltd.     

Development of regional flood frequency analysis techniques using generalized additive models for Australia

Estimation of flood quantiles in ungauged catchments is a common problem in hydrology. For this, the log-linear regression model is widely adopted. However, in many cases, a simple log transformation may not be able to capture the complexity and nonlinearity in flood generation processes. This paper develops generalized additive model (GAM) to deal with nonlinearity between the dependent and predictor variables in regional flood frequency analysis (RFFA) problems. The data from 85 gauged catchments from New South Wales State in Australia is used to compare the performances of a number of alternative RFFA methods with respect to variable selection, variable transformation and delineation of regions. Four RFFA methods are compared in this study: GAM with fixed region, log-linear model, canonical correlation analysis (to form neighbourhood in the space catchment attributes) and region-of-influence approach. Based on the outcome from a leave-one-out validation approach, it has been found that the GAM method generally outperforms the other methods even without linking GAM with a neighbourhood/region-of-influence approach. The main strength of GAM is that it captures the non-linearity between the dependent and predictor variables without any restrictive assumption. The findings of this study will encourage other researchers worldwide to apply GAM in RFFA studies, allowing development of more flexible and realistic RFFA models and their wider adoption in practice.     

Perennial springs provide information to predict low flows in mountain basins

A new method for estimating low flows in ungauged rivers from minimum discharge of perennial springs is proposed. This spring-based approach (SBA) is tested in 21 catchments from the northern Apennines, Italy. First, the hydrogeological behaviour of each geological formation and superficial deposit is related to the spatial distribution and discharge of perennial springs in a test area using a Bayesian approach, weight of evidence (WoE). Second, the observed river flow exceeded for 95% of the observation period is related to the type of geological formations outcropping within the catchment. Finally, the q95 low flows are estimated from the WoE weights. The SBA performance is assessed by leave-one-out cross-validation and compared with the results of a multiple regression (MR) model that accounts for selected catchment characteristics, but no springs. The results show that the SBA outperforms MR. The better performance of the SBA may be related to its ability to capture bedrock characteristics, which are the main controls of low flows in the study area.     

Application of L‐moments and Bayesian inference for low‐flow regionalization in Sefidroud basin,Iran

Reliable estimation of low flows at ungauged catchments is one of the major challenges in water‐resources planning and management. This study aims at providing at‐site and ungauged sites low‐flow frequency analysis using regionalization approach. A two‐stage delineating homogeneous region is proposed in this study. Clustering sites with similar low‐flow L‐moment ratios is initially conducted, and L‐moment‐based discordancy and heterogeneity measures are then used to detect unusual sites. Based on the goodness‐of‐fit test statistic, the best‐fit regional model is identified in each hydrologically homogeneous region. The relationship between mean annual 7‐day minimum flow and hydro‐geomorphic characteristics is also constructed in each homogeneous region associated with the derived regional model for estimating various low‐flow quantiles at ungauged sites. Uncertainty analysis of model parameters and low‐flow estimations is carried out using the Bayesian inference. Applied in Sefidroud basin located in northwestern Iran, two hydrologically homogeneous regions are identified, i.e. the east and west regions. The best‐fit regional model for the east and west regions are generalized logistic and Pearson type III distributions, respectively. The results show that the proposed approach provides reasonably good accuracy for at‐site as well as ungauged‐site frequency analysis. Besides, interval estimations for model parameters and low flows provide uncertainty information, and the results indicate that Bayesian confidence intervals are significantly reduced when comparing with the outcomes of conventional t‐distribution method. Copyright © 2013 John Wiley & Sons, Ltd.