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This paper investigates the impact of the Hungry Horse Dam on streamflow dynamics in the South Fork of the Flathead River, Montana, USA. To this end, pre- and post-dam periods of raw and naturalized streamflow data were analysed. Pettitt’s change point analysis indicated a significant change point in streamflow dynamics due to dam construction. Complexities in the pre- and post-dam periods were evaluated by sample and multi-scale entropy analyses, and the entropies of the post-dam period were found to be higher than those of the pre-dam period. Possible reasons for this, unrelated to the natural hydrological cycle caused by the dam, were analysed using wavelet analyses. The wavelet analyses showed a clear change in the phase relationship between precipitation and streamflow. Finally, weak positive trends found in the hydrological variables indicated the effects of human activities (e.g. dam construction). The results also revealed distorted lead times, which can improve the streamflow forecasts for different lead times. [ABSTRACT FROM PUBLISHER]

In recent years sampling approaches have been used more widely than optimization algorithms to find parameters of conceptual rainfall–runoff models, but the difficulty of calibration of such models remains in dispute. The problem of finding a set of optimal parameters for conceptual rainfall–runoff models is interpreted differently in various studies, ranging from simple to relatively complex and difficult. In many papers, it is claimed that novel calibration approaches, so-called metaheuristics, outperform the older ones when applied to this task, but contradictory opinions are also plentiful. The present study aims at calibration of two simple lumped conceptual hydrological models, HBV and GR4J, by means of a large number of metaheuristic algorithms. The tests are performed on four catchments located in regions with relatively similar climatic conditions, but on different continents. The comparison shows that, although parameters found may somehow differ, the performance criteria achieved with simple lumped models calibrated by various metaheuristics are very similar and differences are insignificant from the hydrological point of view. However, occasionally some algorithms find slightly better solutions than those found by the vast majority of methods. This means that the problem of calibration of simple lumped HBV or GR4J models may be deceptive from the optimization perspective, as the vast majority of algorithms that follow a common evolutionary principle of survival of the fittest lead to sub-optimal solutions. [ABSTRACT FROM PUBLISHER]

There is great potential in Data Assimilation (DA) for the purposes of uncertainty identification, reduction and real-time correction of hydrological models. This paper reviews the latest developments in Kalman filters (KFs), particularly the Extended KF (EKF) and the Ensemble KF (EnKF) in hydrological DA. The hydrological DA targets, methodologies and their applicability are examined. The recent applications of the EKF and EnKF in hydrological DA are summarized and assessed critically. Furthermore, this review highlights the existing challenges in the implementation of the EKF and EnKF, especially error determination and joint parameter estimation. A detailed review of these issues would benefit not only the Kalman-type DA but also provide an important reference to other hydrological DA types. Editor D. Koutsoyiannis; Associate editor F. Pappenberger [ABSTRACT FROM AUTHOR]

Series of observed flood intervals, defined as the time intervals between successive flood peaks over a threshold, were extracted directly from 11 approximately 100-year streamflow datasets from Queensland, Australia. A range of discharge thresholds were analysed that correspond to return periods of approximately 3.7 months to 6.3 years. Flood interval histograms at South East Queensland gauges were consistently unimodal whereas those of the North and Central Queensland sites were often multimodal. The exponential probability distribution (pd) is often used to describe interval exceedence probabilities, but fitting utilizing the Anderson-Darling statistic found little evidence that it is the most suitable. The fatigue life pd dominated subyear return periods (<1 year), often transitioning to a log Pearson 3 pd at above-year return periods. Fatigue life pd is used in analysis of the lifetime to structural failure when a threshold is exceeded, and this paper demonstrates its relevance also to the elapsed time between above-threshold floods. At most sites, the interval medians were substantially less than the means for sub-year return periods. Statistically the median is a better measure of the central tendency of skewed distributions but the mean is generally used in practice to describe the classical concept of flood return period. [ABSTRACT FROM AUTHOR]

This paper evaluates the sensitivity of hydrological projections to the choice of potential evapotranspiration formulas on two natural sub-catchments, in Canada and Germany. Twenty-four equations, representing a large range of options, are applied for calibration over the whole observation time series and for future conditions. The modelling chain is composed of dynamically downscaled climatic projections and a 20-member (ensemble) hydrological model, along with a snow module. The roots of the sensitivity and its propagation within the hydrological chain are evaluated to show influences on climate change impact conclusions. Results show large differences between the 24 simulated potential evapotranspiration time series. However, these discrepancies only moderately affect the calibration efficiency of hydrological models as a result of adaptation of parameters. Choice of formula influences hydrological projections and climate change conclusions for both catchments in terms of simulated and projected values, and also in the magnitude of changes during important dynamic periods such as spring and autumn high flows and summer low flows. Spread of the hydrological response is lower for the combinational formulas than for temperature-based or radiation-based equations. All the results reveal the importance of testing a large spectrum of potential evapotranspiration formulas in a decision-making context, such as water resources management. [ABSTRACT FROM AUTHOR]

The honey-bees mating programming (HBMP) algorithm is introduced as a novel tool for predicting suspended sediment concentration for the Mad River catchment near Arcata, USA. The paper also applies gene expression programming (GEP) as a comparison and shows that these two approaches can the produce transparent, nonlinear relationships between the independent and dependent variables. Some modifications have been made to the HBMP algorithm to improve its capability and efficiency. The results achieved from this method and GEP are compared with two different sediment rating curves based on regression techniques. The findings show that the results from both the HBMP and GEP methods are promising and outperform the results obtained from the sediment rating curves.Editor D. Koutsoyiannis; Associate editor L. See [ABSTRACT FROM AUTHOR]

A significant decrease in mean river flow as well as shifts in flood regimes have been reported at several locations along the River Niger. These changes are the combined effect of persistent droughts, damming and increased consumption of water. Moreover, it is believed that climate change will impact on the hydrological regime of the river in the next decades and exacerbate existing problems. While decision makers and stakeholders are aware of these issues, it is hard for them to figure out what actions should be taken without a quantitative estimate of future changes. In this paper, a Soil and Water Assessment Tool (SWAT) model of the Niger River watershed at Koulikoro was successfully calibrated, then forced with the climate time series of variable length generated by nine regional climate models (RCMs) from the AMMA-ENSEMBLES experiment. The RCMs were run under the SRES A1B emissions scenario. A combination of quantile-quantile transformation and nearest-neighbour search was used to correct biases in the distributions of RCM outputs. Streamflow time series were generated for the 2026–2050 period (all nine RCMs), and for the 2051–2075 and 2076–2100 periods (three out of nine RCMs) based on the availability of RCM simulations. It was found that the quantile-quantile transformation improved the simulation of both precipitation extremes and ratio of monthly dry days/wet days. All RCMs predicted an increase in temperature and solar radiation, and a decrease in average annual relative humidity in all three future periods relative to the 1981–1989 period, but there was no consensus among them about the direction of change of annual average wind speed, precipitation and streamflow. When all model projections were averaged, mean annual precipitation was projected to decrease, while the total precipitation in the flood season (August, September, October) increased, driving the mean annual flow up by 6.9% (2026–2050), 0.9% (2051–2075) and 5.6% (2076–2100). At-test showed that changes in multi-model annual mean flow and annual maximum monthly flow between all four periods were not statistically significant at the 95% confidence level. [ABSTRACT FROM PUBLISHER]

Floods, as extreme hydrological phenomena, can be described by more than one correlated characteristic, such as peak, volume and duration. These characteristics should be jointly considered since they are generally not independent. For an ungauged site, univariate regional flood frequency analysis (FA) provides a limited assessment of flood events. A recent study proposed a procedure for regional FA in a multivariate framework. This procedure represents a multivariate version of the index-flood model and is based on copulas and multivariate quantiles. The performance of the proposed procedure was evaluated by simulation. However, the model was not tested on a real-world case study data. In the present paper, practical aspects are investigated jointly for flood peak (Q) and volume (V) of a dataset from the Côte-Nord region in the province of Quebec, Canada. The application of the proposed procedure requires the identification of the appropriate marginal distribution, the estimation of the index flood and the selection of an appropriate copula. The results of the case study show that the regional bivariate FA procedure performed well. This performance depends strongly on the performance of the two univariate models and, more specifically, the univariate model ofQ. The results show also the impact of the homogeneity of the region on the performance of the univariate and bivariate models.Editor D. Koutsoyiannis [ABSTRACT FROM AUTHOR]

Different sets of parameters and conceptualizations of a basin can give equally good results in terms of predefined objective functions. Therefore, a need exists to tackle equifinality and quantify the uncertainty bands of a model. In this paper we use the concepts of equifinality, identifiability and uncertainty to propose a simple method aimed at constraining the equifinal parameters and reducing the uncertainty bands of model outputs, and obtaining physically possible and reasonable models. Additionally, the uncertainty of equifinal solutions is quantified to estimate the amount by which output uncertainty can be reduced by knowing how to discard most of the equifinal solutions of a model. As a study case, a conceptual model of the Chillán basin in Chile is carried out. From the study it is concluded that using identifiability analysis makes it possible to constrain equifinal solutions with reduced uncertainty and realistic models, resulting in a framework that can be recommended to practitioners, especially due to the simplicity of the method. [ABSTRACT FROM AUTHOR]