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Abstract: Previous studies have shown that it is very difficult to distinguish human-induced changes from those caused by natural forcing. In this paper we try to quantify the influence of land use and water management on flows of the Upper Narew River in north-east Poland. Apart from climatic and land use changes, the Upper Narew catchment was changed by the construction of a storage reservoir at Siemianówka, near Bondary, on the upstream reach of the river. We apply four different approaches to analysing the changes in flow regime and catchment response for the periods before and after reservoir construction. First we estimate the cumulative distribution functions for low and high flow events. The second approach is a time series analysis of flow variation over the whole length of available data and the derivation of cumulative distribution functions for the flows and 0.25–0.75 quantiles followed by a statistical analysis of the number of events below and above the thresholds and their duration. The third approach consists of the application of the Wittenberg baseflow separation method and tests for changes in baseflow indices. In the fourth approach an analysis of changes in flow regime is performed by studying the changes in transfer function-based flow model parameters. Long-term changes in land use are assessed using previous studies of the catchment and the analysis of Corine land cover data and government yearbooks. The results show that different methods explain different aspects of changes in the catchment and flow regime due to climatic changes and changes in land use and water management practices. The analysis of cumulative distribution functions gave evidence of the influence of Siemianówka reservoir on low flows which was also confirmed by the low flow analysis using the Wittenberg approach. The STF analysis of flows indicates the existence of changes in flow regime that can be attributed to the roughness changes in the channel. [Copyright &y& Elsevier]

Abstract: Operational flood forecasting requires accurate forecasts with a suitable lead time, in order to be able to issue appropriate warnings and take appropriate emergency actions. Recent improvements in both flood plain characterization and computational capabilities have made the use of distributed flood inundation models more common. However, problems remain with the application of such models. There are still uncertainties associated with the identifiability of parameters; with the computational burden of calculating distributed estimates of predictive uncertainty; and with the adaptive use of such models for operational, real-time flood inundation forecasting. Moreover, the application of distributed models is complex, costly and requires high degrees of skill. This paper presents an alternative to distributed inundation models for real-time flood forecasting that provides fast and accurate, medium to short-term forecasts. The Data Based Mechanistic (DBM) methodology exploits a State Dependent Parameter (SDP) modelling approach to derive a nonlinear dependence between the water levels measured at gauging stations along the river. The transformation of water levels depends on the relative geometry of the channel cross-sections, without the need to apply rating curve transformations to the discharge. The relationship obtained is used to transform water levels as an input to a linear, on-line, real-time and adaptive stochastic DBM model. The approach provides an estimate of the prediction uncertainties, including allowing for heterescadasticity of the multi-step-ahead forecasting errors. The approach is illustrated using an 80km reach of the River Severn, in the UK. [Copyright &y& Elsevier]

Abstract: The paper presents an application of the generalised likelihood uncertainty estimation methodology to the problem of estimating the uncertainty of predictions produced by environmental models. The methodology is placed in a wider context of different approaches to inverse modelling and, in particular, a comparison is made with Bayesian estimation techniques based on explicit structural assumptions about model error. Using a simple example of a rainfall-flow model, different evaluation measures and their influence on the prediction uncertainty and credibility intervals are demonstrated. [Copyright &y& Elsevier]

Abstract: This paper shows that maximum snow depth and the length of accumulation and ablation periods observed at the local scale of Hornsund, SW Spitsbergen, are partly explained by monthly and seasonal values of the AO and NAO indices in the given and previous hydrological years. This analysis is followed by an application of a statistically efficient lumped parameter time series approach to modelling the dynamics of snow depth, based on daily meteorological and snow depth measurements from the same area. A dynamic Stochastic Transfer Function (STF) model is developed that follows the Data Based Mechanistic approach, where a stochastic data-based identification of model structure and an estimation of its parameters are followed by a physical interpretation. Apart from snow depth estimates, the model provides also the uncertainty limits. An analysis of the variation in parameter estimates over the whole measurement period provides an insight into the possible influence of recent climate change on snow cover dynamics at Hornsund. To help explain the physical meaning of the model parameters, we classified the data into accumulation and ablation periods. The models were run for each period separately. The first order model structure was found to be the most suitable to explain the variability of the snow cover. The cross-validation of models performance on the other years shows that the predictive value of the obtained models is not very consistent, with a mixture of good and bad years. The analysis shows that variability in the NAO and AO indices, reflecting the changes in global circulation patterns, is reproduced by local, physically meaningful, STF model-derived parameters in the form of residence times and temperature and precipitation related gains. [Copyright &y& Elsevier]