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This paper presents the background, objectives, and preliminary outcomes from the first year of activities of the Polish-Norwegian project CHIHE (Climate Change Impact on Hydrological Extremes). The project aims to estimate the influence of climate changes on extreme river flows (low and high) and to evaluate the impact on the frequency of occurrence of hydrological extremes. Eight "twinned" catchments in Poland and Norway serve as case studies. We present the procedures of the catchment selection applied in Norway and Poland and a database consisting of near-natural ten Polish and eight Norwegian catchments constructed for the purpose of climate impact assessment. Climate projections for selected catchments are described and compared with observations of temperature and precipitation available for the reference period. Future changes based on those projections are analysed and assessed for two periods, the near future (2021-2050) and the far-future (2071-2100). The results indicate increases in precipitation and temperature in the periods and regions studied both in Poland and Norway. [ABSTRACT FROM AUTHOR]

An HBV rainfall–runoff model was applied to test the influence of climatic characteristics on model parameter values. The methodology consisted of the calibration and cross-validation of the HBV model on a series of 5-year periods for four selected catchments (Axe, Kamp, Wieprz and Wimmera). The model parameters were optimized using the SCEM-UA method which allowed for their uncertainty also to be assessed. Nine climatic indices were selected for the analysis of their influence on model parameters, and divided into water-related and temperature-related indices. This allowed the dependence of HBV model parameters on climate characteristics to be explored following their response to climate change conditioned on the catchment’s physical characteristics. The Pearson correlation coefficient and weighted Pearson correlation coefficient were used to test the dependence. Most parameters showed a statistically significant dependence on several climatic indices in all catchments. The study shows that the results of the correlation analysis with and without parametric uncertainty taken into account differ significantly. [ABSTRACT FROM AUTHOR]

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]

Summary: Analysis of climate change scenarios indicates an increased probability of high rainfalls and related flooding. Climate change may also lead to an increase in rainfall variability and the increased probability of otherwise extreme events. This forces researchers to focus on both high and low flow models. In addition, changes in soil management and an increase in water demand due to growing industrial and domestic requirements are adding to water scarcity. Sustainable water management is not possible without a better understanding of water balance in the catchment and better modelling tools for low flows. Moreover, application to climate change requires the development of a simulation model. This paper aims to present a Data Based Mechanistic approach to flow modelling, with special emphasis on low flows. The model applies a Stochastic Transfer Function approach to log-transformed flow. In this respect, the new model has certain interesting similarities with TOPMODEL which assumes an exponential relationship between soil moisture storage and flow. The paper explores these similarities, gives a physical explanation of the model structure and estimates model predictive uncertainty. A physical interpretation of the model allows the ranges of its applicability to be stated. The approach is tested on two catchments 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: A parametrically parsimonious, data-based model was built on observations at hillslope and catchment scale to simulate the distribution of overland flow within a small East African Highlands catchment (Kwalei, Tanzania). A rainfall-flow Data Based Mechanistic model identified catchment effective rainfall and separated the discharge quick flow, interpreted as the combination of overland flow plus reinfiltration along the slopes, and the slow flow, interpreted as ground water displacement. Observations of overland flow occurrence along the slopes were used to derive probability distribution functions (pdfs) of overland flow in relation to effective rainfall for two pre-defined hydrologic response units (HRUs): perennial (HRU_1) versus other crops (HRU_2). At low effective rainfall, overland flow was more frequent in HRU_2, while at high effective rainfall overland flow in the two HRUs was similar. The pdfs were employed to disaggregate the quick flow into HRU overland flow depth. Reinfiltration was accounted for by assuming that only the overland flow generated in the lower part of the field would drain downslope. Effective reinfiltration length was about 4m. Comparison of model simulations versus Gerlach trough measurements indicated that rainfall intensity was not accounted for sufficiently. The use of smaller time steps or, alternatively, of a rainfall intensity threshold could improve model performance. However, given the high variability of overland flow observed along the slopes and the limited dataset, model simulations were considered satisfactory. Though the model needs further testing on other datasets, the disaggregating approach represents an inductive alternative to the use of infiltration equations to model overland flow within a catchment. [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]

Abstract: Distributed erosion models are potentially good tools for locating soil sediment sources and guiding efficient Soil and Water Conservation (SWC) planning, but the uncertainty of model predictions may be high. In this study, the distribution of erosion within a catchment was predicted with a semi-empirical erosion model that combined a semi-distributed hydrological model with the Morgan, Morgan and Finney (MMF) empirical erosion model. The model was tested in a small catchment of the West Usambara Mountains (Kwalei catchment, Tanzania). Soil detachability rates measured in splash cups (0.48–1.16gJ−1) were close to model simulations (0.30–0.35gJ−1). Net erosion rates measured in Gerlach troughs (0.01–1.05kgm−2 per event) were used to calibrate the sediment transport capacity of overland flow. Uncertainties of model simulations due to parameterisation of overland flow sediment transport capacity were assessed with the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. The quality of the spatial predictions was assessed by comparing the simulated erosion pattern with the field-observed erosion pattern, measuring the agreement with the weighted Kappa coefficient of the contingency table. Behavioural parameter sets (weighted Kappa >0.50) were those with short reinfiltration length (<1.5m) and ratio of overland flow power α to local topography power γ close to 0.5. In the dynamic Hortonian hydrologic regime and the dissected terrain of Kwalei catchment, topography controlled the distribution of erosion more than overland flow. Simulated erosion rates varied from −4 to +2kgm−2 per season. The model simulated correctly around 75% of erosion pattern. The uncertainty of model predictions due to sediment transport capacity was high; around 10% of the fields were attributed to either slight or severe erosion. The difficult characterisation of catchment-scale effective sediment transport capacity parameters poses a major limit to distributed erosion modelling predicting capabilities. [Copyright &y& Elsevier]