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This article presents the results of a geophysical survey from which detailed images of glacial and periglacial landforms and subsurface structures were obtained. Sediments and landforms on newly deglaciated terrain can be used to reconstruct the extent and character of glaciers in the past and add to the understanding of their response to climate and environmental changes. To derive spatial information from complex geomorphological terrain, joint interpretation of three non-intrusive geophysical methods were applied: Electrical Resistivity Tomography (ERT), Ground Penetrating Radar (GPR), and time-lapse Seismic Tomography. These were used to identify subsurface structures in the forefield of the retreating Hans Glacier in SW Spitsbergen, Svalbard. Three main zones were distinguished and described: outwash plain, terminal moraine from the last glacial maximum, and glacial forefield proximal to the glacier front. Geophysical profiles across these zones reveal information on glacio-fluvial sediment thickness and structure, ice thickness and structure, and bedrock topography. The freezing-thawing effect of the active layer has a strong and deep impact, as demonstrated by variations in P-wave velocity in the obtained outcomes. The results are discussed in the context of the current climate in Svalbard. This study provides a snapshot of ground parameters and the current state of the subsurface in southern Spitsbergen. The boundary between sediment-bedrock layers was estimated to be from 5 to 20 m in depth. It is the first such extensive description of periglacial structures in the forefield of the Hans Glacier, utilising the longest ERT profile (1500 m) in Svalbard together with deep GPR and precise seismic tomography. [ABSTRACT FROM AUTHOR]

The article presents the climatological dataset from the Polish Polar Station Hornsund located in the southwest part of Spitsbergen – the biggest island of the Svalbard archipelago. Due to a general lack of long-term in situ measurements and observations, the High Arctic remains one of the largest climate-data-deficient regions on the Earth. Therefore, the described time series of observations in this paper are of unique value. To draw conclusions on the climatic changes in the Arctic, it is necessary to analyse and compare the long-term series of continuous, in situ observations from different locations, rather than relying on the climatic simulations only. In recent decades, rapid environmental changes occurring in the Atlantic sector of the Arctic are reflected in the data series collected by the operational monitoring conducted at the Hornsund station. We demonstrate the results of the 40-year-long series of observations. Climatological mean values or totals are given, and we also examined the variability of meteorological variables at monthly and annual scale using the modified Mann–Kendall test for trend and Sen's method. The relevant daily, monthly, and annual data are provided on the PANGAEA repository (10.1594/PANGAEA.909042, Wawrzyniak and Osuch, 2019). [ABSTRACT FROM AUTHOR]

The article presents the climatological dataset from the Polish Polar Station Hornsund located in the SW part of Spitsbergen - the biggest island of the Svalbard Archipelago. Due to a general lack of long-term in situ measurements and observations, the high Arctic remains one of the largest climate‐data deficient regions on the Earth, so described series is of unique value. To draw conclusions on the climatic changes in the Arctic, it is necessary to analyse the long-term series of continuous, systematic, in situ observations from different locations and comparing the corresponding data, rather than rely on the climatic simulations only. In recent decades, rapid environmental changes occurring in the Atlantic sector of the Arctic are reflected in the data series collected by the operational monitoring conducted at the Hornsund Station. We demonstrate the results of the 40 years-long series of observations. Climatological mean values or totals are given, and we also examined the variability of meteorological variables at monthly and annual scale using the modified Mann-Kendall test for trend and Sen’s method. The relevant daily, monthly, and annual data are provided on the PANGAEA repository (https://doi.org/10.1594/PANGAEA.909042, Wawrzyniak and Osuch, 2019). [ABSTRACT FROM AUTHOR]

Changes in active layer thickness (ALT) over Arctic and permafrost regions have an important impact on rainfall-runoff transformation. General warming is observed across Svalbard Archipelago and corresponds to increases in ground temperatures. Permafrost thaw and changes in ALT due to climate warming alter how water is routed and stored in catchments, and thus impact both surface and subsurface processes. The overall aim of the present study is to examine the relationships between temporal changes of active layer depth and hydrological model parameters, together with variation in the catchment response. The analysis was carried out for the small unglaciated catchment Fuglebekken, located in the vicinity of the Polish Polar Station Hornsund on Spitsbergen. For hydrological modelling, the conceptual rainfall-runoff HBV (Hydrologiska Byråns Vattenbalansavdelning) model was used. The model was calibrated and validated on runoff within subperiods. A moving window approach (3 weeks long) was applied to derive temporal variation of parameters. Model calibration, together with an estimation of parametric uncertainty, was carried out using the Shuffled Complex Evolution Metropolis algorithm. This allowed the dependence of HBV model parameters on ALT to be analysed. Also, we tested the influence of model simplification, correction of precipitation, and initial conditions on the modelling results. [ABSTRACT FROM AUTHOR]

Changes in low flow indices under future climates are estimated for eight catchments in Poland. A simulation approach is used to derive daily flows under changing climatic conditions, following RCP 4.5 and RCP 8.5 emission scenarios. The HBV rainfall-runoff model is used to simulate low flows. The model is calibrated and validated using streamflow observations from periods 1971-2000 and 2001-2010. Two objective functions are used for calibration: Nash-Sutcliffe and log transformed Nash-Sutcliffe. Finally, the models are run using the bias-corrected precipitation and temperature data simulated by GCM/RCM models for the periods 2021-2050 and 2071-2100. We estimate low flow indices for the simulated time series, including annual minima of 7-day mean river flows and number, severity and duration of low flow events. We quantify the biases of low flow indices by N-way analysis of variance (ANOVA) analysis and Tukey test. Results indicate a large effect of climate models, as well as objective functions, on the low flow indices obtained. A comparison of indices from the two future periods with the reference period 1971-2000 confirms the trends obtained in previous studies, in the form of a projected decrease in the frequency and intensity of low flow events. [ABSTRACT FROM AUTHOR]

The aim of this study is to estimate likely changes in flood indices under a future climate and to assess the uncertainty in these estimates for selected catchments in Poland. Precipitation and temperature time series from climate simulations from the EURO-CORDEX initiative for the periods 1971-2000, 2021-2050 and 2071-2100 following the RCP4.5 and RCP8.5 emission scenarios have been used to produce hydrological simulations based on the HBV hydrological model. As the climate model outputs for Poland are highly biased, post processing in the form of bias correction was first performed so that the climate time series could be applied in hydrological simulations at a catchment-scale. The results indicate that bias correction significantly improves flow simulations and estimated flood indices based on comparisons with simulations from observed climate data for the control period. The estimated changes in the mean annual flood and in flood quantiles under a future climate indicate a large spread in the estimates both within and between the catchments. An ANOVA analysis was used to assess the relative contributions of the 2 emission scenarios, the 7 climate models and the 4 bias correction methods to the total spread in the projected changes in extreme river flow indices for each catchment. The analysis indicates that the differences between climate models generally make the largest contribution to the spread in the ensemble of the three factors considered. The results for bias corrected data show small differences between the four bias correction methods considered, and, in contrast with the results for uncorrected simulations, project increases in flood indices for most catchments under a future climate. [ABSTRACT FROM AUTHOR]

In this study, seasonality and interannual variability of snow depth at two stations (Hornsund and Barentsburg) located in western Spitsbergen are investigated. For this purpose, the novel Moving Average over Shifting Horizon method combined with trend estimation was used. The Hornsund and Barentsburg stations exhibit similar snow depth trends during early autumn and late spring when statistically significant decreases were estimated at both stations (for August 1984–July 2016). In the remaining period, there are differences in outcomes between stations. The results for Barentsburg from October till the end of May are characterised by the lack of a trend while at Hornsund decreases of snow depth were estimated. The largest changes occur in May when the snow depth was at its maximum. Differences in the estimated tendencies were explained with the help of a trend analysis for air temperature and precipitation. An analysis of maximum snow depth, snow onset date, snow disappearance date and snow-cover duration is included. The results of the assessment depend on the location, with a lack of statistically significant changes in Barentsburg, and later snow onset date, shorter duration and decrease of maximum depth in Hornsund. [ABSTRACT FROM AUTHOR]

The goal of this study is to test applicability of the conceptual catchment run-off HBV model to simulate discharge in small non-glaciated Arctic catchment. Within two ablation seasons, 2014 and 2015, in the Fuglebekken catchment (Spitsbergen, Svalbard), selected hydro-meteorological measurements were conducted, including discharge measurements in 10 min interval by Nivus PCM-F device with active Doppler sensor. The model parameters were calibrated on discharge measurements from both years separately and verified independently. As the transformation from rainfall to runoff includes a number of processes with different dynamics and timescales, the proper description of the processes and their simulation of discharge depend on the temporal resolution of the data. For that purpose, the relationships between the calibration and validation results, and optimal model parameters with different time steps were analyzed. It was found that calculated fit of simulated to observed discharge, depends on the year, time step and data averaging. The best results were obtained for the model from year 2015 for 3 and 6 h using averaged input data. [ABSTRACT FROM PUBLISHER]

ABSTRACT Natural systems are characterized by seasonal and inter-annual variability. In the Arctic, changes in annual runs of air temperature and precipitation play crucial roles and have implications for the abiotic and biotic environment. In our study, we investigate seasonality and inter-annual variability of air temperature and precipitation at three stations with the longest diurnal instrumental records in Spitsbergen: the Polish Polar Station Hornsund, Longyearbyen Airport and Ny-Ålesund. For that purpose, we used the novel method of analysis Moving Average over Shifting Horizon ( MASH), combined with trend estimation. The application of MASH helps detecting changes of seasonality in the data by filtering out the effects of periodicity of the variables. The results show that the tendencies of changes are not evenly distributed throughout the year. In the case of air temperature, upward trends were detected, with the most pronounced changes in the freezing season and slight changes during summer. The shape of the annual run has also changed, with a shift of the coldest months from December and January to March and April. In terms of precipitation, the highest increases were obtained for the late melting season, autumn and early winter, especially pronounced in Hornsund and Ny-Ålesund. Changes in daily air temperature and precipitation totals were strongly linked to sea ice conditions. An influence of other climatic indices was also statistically significant, but varied depending on the month. [ABSTRACT FROM AUTHOR]

The aim of this study was to provide an estimation of climate variability in the Hornsund area in Southern Spitsbergen in the period 1976-2100. The climatic variables were obtained from the Polar-CORDEX initiative in the form of time series of daily air temperature and precipitation derived from four global circulation models (GCMs) following representative concentration pathways (RCP) RCP 4.5 and RCP 8.5 emission scenarios. In the first stage of the analysis, simulations for the reference period from 1979 to 2005 were compared with observations at the Polish Polar Station Hornsund from the same period of time. In the second step, climatic projections were derived and monthly and annual means/sums were analysed as climatic indices. Following the standard methods of trend analysis, the changes of these indices over three time periods - the reference period 1976-2005, the near-future period 2021-2050, and far-future period 2071-2100 - were examined. The projections of air temperature were consistent. All analysed climate models simulated an increase of air temperature with time. Analyses of changes at a monthly scale indicated that the largest increases were estimated for winter months (more than 11°C for the far future using the RCP 8.5 scenario). The analyses of monthly and annual sums of precipitation also indicated increasing tendencies for changes with time, with the differences between mean monthly sums of precipitation for the near future and the reference period similar for each months. In the case of changes between far future and reference periods, the highest increases were projected for the winter months. [ABSTRACT FROM AUTHOR]

The thermal state of permafrost is a crucial indicator of environmental changes occurring in the Arctic. The monitoring of ground temperatures in Svalbard has been carried out in instrumented boreholes, although only few are deeper than 10 m and none are located in southern part of Spitsbergen. Only one of them, Janssonhaugen, located in central part of the island, provides the ground temperature data down to 100 m. Recent studies have proved that significant warming of the ground surface temperatures, observed especially in the last three decades, can be detected not only just few meters below the surface, but reaches much deeper layers. The aim of this paper is evaluation of the permafrost state in the vicinity of the Polish Polar Station in Hornsund using the numerical heat transfer model CryoGrid 2. The model is calibrated with ground temperature data collected from a 2 m deep borehole established in 2013 and then validated with data from the period 1990-2014 from five depths up to 1 m, measured routinely at the Hornsund meteorological station. The study estimates modelled ground thermal profile down to 100 m in depth and presents the evolution of the ground thermal regime in the last 25 years. The simulated subsurface temperature trumpet shows that multiannual variability in that period can reach 25 m in depth. The changes of the ground thermal regime correspond to an increasing trend of air temperatures observed in Hornsund and general warming across Svalbard. [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]

An analysis of a suite of climatological indices was undertaken on the basis of long-term (1979-2019) climatological data from the Polish Polar Station in Hornsund, SW Spitsbergen. It was followed by an attempt to assess the scale of their impact on the local environment. The temperature and precipitation indices were based on percentiles of the variables calculated for a population of daily values from the climate normals for 1981-2010. A greater share of both cyclonic and anticyclonic circulations from the S and SW sectors, forcing the advection of warm air masses from the south, was decisive for the trends of change in comparison with the long-term mean. Both extreme precipitation and drought events depend on the 500 hPa geopotential height and precipitable water anomalies, determined by the baric field over the North Atlantic. Climate changes impact on the dynamics of local geoecosystems by causing faster glacier ablation and retreat, permafrost degradation, intensification of the hydrological cycle in glaciated and unglaciated catchments, and changes in the condition and growth of tundra vegetation. [ABSTRACT FROM AUTHOR]

In the age of remote sensing, particularly with new generation Uncrewed Aerial Vehicles (UAVs), there is a broad spectrum of applications, especially in remote and rapidly changing areas such as the Arctic. Due to challenging conditions in this region, there is a scarcity of detailed spatial studies with data that may be used to estimate changes in glacier volume and geomorphological changes caused by permafrost freeze–thaw cycles. Drone-based Digital Elevation Models (DEM) offer a finer spatial resolution with higher accuracy than airborne and satellite-based products that can be used for acquiring, interpreting, and precisely representing spatial data in broad studies. In this study, we evaluate a UAV-based DEM of two High Arctic catchments, Fuglebekken and Ariebekken, located on Spitsbergen Island. The surveys were carried out in July 2022 using a DJI Matrice 300 RTK drone equipped with a photogrammetric Zenmuse P1 camera. A total of 371 images were taken, covering an area of 7.81 km2. The DEM was created by the Structure-from-Motion technique and achieved a centimetre-level accuracy by overlapping very high-resolution images. The final resolution of the DEM was found to be 0.06 m in Fuglebekken and 0.07 m in Ariebekken, with a horizontal and vertical RMSE of 0.09 m and 0.20 m, respectively. The DJI Matrice 300 RTK drone-based DEM is compared and correlated with the aerial mission of the Svalbard Integrated Arctic Earth Observing System (SIOS) conducted in July 2020 and the satellite-based ArcticDEM acquired in July 2018. This allowed the detection of elevation changes and identification of landscape evolution, such as moraine breaches and coastal erosion. We also highlight the usage of DEM in providing detailed morphometric characteristics and hydrological parameters, such as the delineation of catchments and stream channels. The final products are available at the IG PAS Data Portal. [ABSTRACT FROM AUTHOR]

Abstract: Nature-inspired metaheuristics found various applications in different fields of science, including the problem of artificial neural networks (ANN) training. However, very versatile opinions regarding the performance of metaheuristics applied to ANN training may be found in the literature. Both nature-inspired metaheuristics and ANNs are widely applied to various geophysical and environmental problems. Among them the water temperature forecasting in a natural river, especially in colder climate zones where the seasonality plays important role, is of great importance, as water temperature has strong impact on aquatic life and chemistry. As the impact of possible future climate change on water temperature is not trivial, models are needed to allow projection of streamwater temperature based on simple hydro-meteorological variables. In this paper the detailed comparison of the performance of nature-inspired optimization methods and Levenberg–Marquardt (LM) algorithm in ANNs training is performed, based on the case study of water temperature forecasting in a natural stream, namely Biala Tarnowska river in southern Poland. Over 50 variants of 22 various metaheuristics, including a large number of Differential Evolution, as well as some Particle Swarm Optimization, Evolution Strategies, multialgorithms and Direct Search methods are compared with LM algorithm on ANN training for the described case study. The impact of population size and some control parameters of particular metaheuristics on the ANN training performance are verified. It is found that despite widely claimed large improvement in nature-inspired methods during last years, the vast majority of them are still outperformed by LM algorithm on the selected problem. The only methods that, based on this case study, seem competitive to LM algorithm in terms of the final performance (but not speed) are Differential Evolution algorithms that benefit from the concept of Global and Local neighborhood-based mutation operators. The streamwater forecasting performance of the neural networks is adequate, the major prediction errors are related to the river freezing and melting processes that occur during winter in the mountainous catchment under study. [Copyright &y& Elsevier]

Despite the development of new measuring techniques, monitoring systems and advances in computer technology, rainfall-flow modelling is still a challenge. The reasons are multiple and fairly well known. They include the distributed, heterogeneous nature of the environmental variables affecting flow from the catchment. These are precipitation, evapotranspiration and in some seasons and catchments in Poland, snow melt also. This paper presents a review of work done on the calibration and validation of rainfall-runoff modelling, with a focus on the conceptual HBV model. We give a synthesis of the problems and propose a practical guide to the calibration and validation of rainfall-runoff models. [ABSTRACT FROM AUTHOR]

A methodology to derive solute transport models at any flow rate is presented. The novelty of the proposed approach lies in the assessment of uncertainty of predictions that incorporate parameterisation based on flow rate. A simple treatment of uncertainty takes into account heteroscedastic modelling errors related to tracer experiments performed over a range of flow rates, as well as the uncertainty of the observed flow rates themselves. The proposed approach is illustrated using two models for the transport of a conservative solute: a physically based, deterministic, advection-dispersion model (ADE), and a stochastic, transfer function based, active mixing volume model (AMV). For both models the uncertainty of any parameter increases with increasing flow rate (reflecting the heteroscedastic treatment of modelling errors at different observed flow rates), but in contrast the uncertainty of travel time, computed from the predicted model parameters, was found to decrease with increasing flow rate. [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: 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]

• Depending on stream temperature model, different warming of streamwater is projected. • Projection from particular model occasionally differ from the majority of models. • A number of stream temperature models should be used together for future climates. • Stream temperature in analyzed streams is to be warmed by 2–3.5 °C by 2100 (RCP 8.5). • In specific streams marginal cooling in projected in some months until 2050. In the majority of studies aiming at stream temperature warming due to climate change just a single water temperature model is used. Choosing a single model may highly impact the conclusions from the study. In this paper four relatively different empirical or semi-empirical models: perceptron neural networks, product unit networks, extended logistic regression and air2stream were applied to project the impact of climate change on water temperature in rivers located in temperate climatic zones of the USA and Poland. The models were driven by daily air temperature and streamflow projected by the rainfall-runoff model. In the first step, the models were calibrated and validated. Then the projections of water temperature were derived for the historical periods and two future periods taking into account: (a) climate simulations from the CORDEX initiative (NA-CORDEX and EURO-CORDEX), (b) the GR4J rainfall-runoff model and (c) different water temperature models. The obtained results indicate that due to global warming, the stream temperatures are expected to increase by about 1–2 °C for 2021–2050 and by 2–3 °C for 2071–2100 periods. These changes are not uniformly distributed throughout the year. The largest warming in the USA is found in the summertime, in Poland – in spring and autumn. For some months the discrepancies in the projected stream temperature between various stream temperature models are large. Product unit neural network, logistic regression-based model or air2stream occasionally led to projections that differ from those obtained by the majority of models even by 2 °C. We strongly recommend using at least a few stream temperature models for analysing the impact of climate change on water temperatures or the fate of the aquatic ecosystem. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) become widely used for runoff forecasting in numerous studies. Usually classical gradient-based methods are applied in ANN training and a single ANN model is used. To improve the modelling performance, in some papers ensemble aggregation approaches are used whilst in others, novel training methods are proposed. In this study, the usefulness of both concepts is analysed. First, the applicability of a large number of population-based metaheuristics to ANN training for runoff forecasting is tested on data collected from four catchments, namely upper Annapolis (Nova Scotia, Canada), Biala Tarnowska (Poland), upper Allier (France) and Axe Creek (Victoria, Australia). Then, the importance of the search for novel training methods is compared with the importance of the use of a very simple ANN ensemble aggregation approach. It is shown that although some metaheuristics may slightly outperform the classical gradient-based Levenberg-Marquardt algorithm for a specific catchment, none performs better for the majority of the tested ones. One may also point out a few metaheuristics that do not suit ANN training at all. On the other hand, application of even the simplest ensemble aggregation approach clearly improves the results when the ensemble members are trained by any suitable algorithms.EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR E. Toth [ABSTRACT FROM AUTHOR]

Intermittent rivers are prevalent in many countries across Europe, but little is known about the temporal evolution of intermittence and its relationship with climate variability. Trend analysis of the annual and seasonal number of zero-flow days, the maximum duration of dry spells and the mean date of the zero-flow events is performed on a database of 452 rivers with varying degrees of intermittence between 1970 and 2010. The relationships between flow intermittence and climate are investigated using the standardized precipitation evapotranspiration index (SPEI) and climate indices describing large-scale atmospheric circulation. The results indicate a strong spatial variability of the seasonal patterns of intermittence and the annual and seasonal number of zero-flow days, highlighting the controls exerted by local catchment properties. Most of the detected trends indicate an increasing number of zero-flow days, which also tend to occur earlier in the year, particularly in southern Europe. The SPEI is found to be strongly related to the annual and seasonal zero-flow day occurrence in more than half of the stations for different accumulation times between 12 and 24 months. Conversely, there is a weaker dependence of river intermittence with large-scale circulation indices. Overall, these results suggest increased water stress in intermittent rivers that may affect their biota and biochemistry and also reduce available water resources. [ABSTRACT FROM AUTHOR]

River flow projections for two future time horizons and RCP 8.5 scenario, generated by two projects (CHASE-PL and CHIHE) in the Polish-Norwegian Research Programme, were compared. The projects employed different hydrological models over different spatial domains. The semi-distributed, process-based, SWAT model was used in the CHASE-PL project for the entire Vistula and Odra basins area, whilst the lumped, conceptual, HBV model was used in the CHIHE project for eight Polish catchments, for which the comparison study was made. Climate projections in both studies originated from the common EURO-CORDEX dataset, but they were different, e.g. due to different bias correction approaches. Increases in mean annual and seasonal flows were projected in both studies, yet the magnitudes of changes were largely different, in particular for the lowland catchments in the far future. The HBV-based increases were significantly higher in the latter case than the SWAT-based increases in all seasons except winter. Uncertainty in projections is high and creates a problem for practitioners. [ABSTRACT FROM AUTHOR]

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]

The effect of methods of statistical downscaling of daily precipitation on changes in extreme flow indices under a plausible future climate change scenario was investigated in 11 catchments selected from 9 countries in different parts of Europe. The catchments vary from 67 to 6171 km 2 in size and cover different climate zones. 15 regional climate model outputs and 8 different statistical downscaling methods, which are broadly categorized as change factor and bias correction based methods, were used for the comparative analyses. Different hydrological models were implemented in different catchments to simulate daily runoff. A set of flood indices were derived from daily flows and their changes have been evaluated by comparing their values derived from simulations corresponding to the current and future climate. Most of the implemented downscaling methods project an increase in the extreme flow indices in most of the catchments. The catchments where the extremes are expected to increase have a rainfall-dominated flood regime. In these catchments, the downscaling methods also project an increase in the extreme precipitation in the seasons when the extreme flows occur. In catchments where the flooding is mainly caused by spring/summer snowmelt, the downscaling methods project a decrease in the extreme flows in three of the four catchments considered. A major portion of the variability in the projected changes in the extreme flow indices is attributable to the variability of the climate model ensemble, although the statistical downscaling methods contribute 35–60% of the total variance. [ABSTRACT FROM AUTHOR]

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]

Summary A number of methods have been proposed for the prediction of streamwater temperature based on various meteorological and hydrological variables. The present study shows a comparison of few types of data-driven neural networks (multi-layer perceptron, product-units, adaptive-network-based fuzzy inference systems and wavelet neural networks) and nearest neighbour approach for short time streamwater temperature predictions in two natural catchments (mountainous and lowland) located in temperate climate zone, with snowy winters and hot summers. To allow wide applicability of such models, autoregressive inputs are not used and only easily available measurements are considered. Each neural network type is calibrated independently 100 times and the mean, median and standard deviation of the results are used for the comparison. Finally, the ensemble aggregation approach is tested. The results show that simple and popular multi-layer perceptron neural networks are in most cases not outperformed by more complex and advanced models. The choice of neural network is dependent on the way the models are compared. This may be a warning for anyone who wish to promote own models, that their superiority should be verified in different ways. The best results are obtained when mean, maximum and minimum daily air temperatures from the previous days are used as inputs, together with the current runoff and declination of the Sun from two recent days. The ensemble aggregation approach allows reducing the mean square error up to several percent, depending on the case, and noticeably diminishes differences in modelling performance obtained by various neural network types. [ABSTRACT FROM AUTHOR]

In recent years, rapid climatic changes and their impact are widely visible and recognizable around the world. The Atlantic sector of the Arctic is the place of the strongest observed changes. As a result, such changes are already destabilizing the arctic systems including the glaciers and the permafrost that strongly affects the Arctic's physical and biological systems. In the presented work, we applied multiple geophysical methods and tools, to recognize horizontal and vertical distribution as well as ongoing changes in the seasonally and perennially frozen ground. The study site, located near the Polish Polar Station in the Hornsund (Svalbard), is unique due to its location between sea-shore and mountainous ridges and close presence of the retreating Hans Glacier. Such an environment allows for conducting research encompassing various dynamical cryospheric, geological and other environmental processes. The monitoring of the ground temperature variations in the several boreholes, with detailed ERT, GPR and MASW modeling, allow for recognition and analyses of the active layer spatial variability and the permafrost changes in this area. The seismic recognition, based on the dense 2D seismic reflection and refraction methods, allows for the direct comparisons between observations conducted during the summer and winter seasons. Results obtained by those methods are directly targeted to visualize not only the active layer thickness but also the permafrost which until today is unknown in the area of Southern Spitsbergen. Additionally, the comparison of the data-set quality between two seasons allowed to select the best conditions for future data acquisition. The recognition of vertical and horizontal changes of the permafrost as well as the active layer depth provided unique information about the thermal ground conditions. Obtained results, gives us the opportunity for explanation of seasonal changes which were observed, measured, and modeled. This information allows for better understanding of the geophysical processes responsible for the cryospheric and geological processes occurring in the study site, and further better estimation of the climate change impacts on the environment SW Spitsbergen. This research was funded by National Science Centre, Poland (NCN) Grant UMO-2016/21/B/ST10/02509. Part of this work was supported within statutory activities No. 3841/E-41/S/2018 of the Ministry of Science and Higher Education of Poland. [ABSTRACT FROM AUTHOR]

Within the SMIRES COST Action (Science and Management of Intermittent Rivers & Ephemeral Streams, www.smires.eu), examples of intermittent rivers across Europe were collected, including gauged catchments with both natural and highly influenced river flow regimes. The examples will be published in a catalogue to give a good overview of the variety of intermittent streams in Europe. Information on the gauged intermittent rivers is summarised in a two-page template: The first page of the template describes the main characteristics of the catchments (land-use, geology, climate, etc.) and the river flow regime. Two panels display hydrographs and flow durations curves and a table gives metrics specific to river flow intermittence relevant for ecology. These hydrological indices have been computed by the SMIRES R package (https://homepage.boku.ac.at/h0540352/smires/framework.html) applied to daily discharge data. The second page of the template is dedicated to the description and reasons for intermittence. A short description about the spatio-temporal pattern of zero-flow events (encompassing seasonal behaviour, observed long-term trends, locations with frequently observed zero-flow events along the river network, etc.). The monitoring network, including gauging stations and other types of observations (e.g. visual inspection of the flow states at different locations along the river) in the catchment, is also described. This presentation gives examples of the pages in the catalogue and a preliminary classification of intermittent rivers at the European scale. [ABSTRACT FROM AUTHOR]