Studies on Intercorrelation between Ions Co-Occurring in Precipitation in the Gdańsk-Sopot-Gdynia Tricity (Poland)

The paper presents monitoring results and environmental pollution assessment for the Gdask-Sopot-Gdynia Tricity (Poland), based onanalysis of precipitation. Precipitation samples were collected over a period of 12 months (January–December 1998) at ten locations in the Tricity. The following selected ions were determined in the samples:SO₄ ^2–, F^–, Cl^–, NO₃ ^–,PO₄ ^3–, NH₄ ⁺, Na⁺,Mg²⁺, Ca²⁺, K⁺. The results were subjected to full statistical evaluation. Values of the parameters determined were correlated with each other. An attempt was made to explain co-occurrences of certain ions and the significance of their mutual effects. Pollutant concentrations and loads in precipitation were also correlated with data on wind direction and temperature in the region.Deposition of pollutants was very high in spring due to the prevailing air circulation patterns and low temperatures. Analysis of the correlations between co-occurring ions confirmed the significant impact of the location (sea coast) on the composition of rain water. Ionic ratios in rainwater were similar to those observed for sea salt samples. In addition, heavy traffic was most probably responsible for high concentrations of various forms of nitrogen and sulphates in the vicinity of major highways.     

Are modern metaheuristics successful in calibrating simple conceptual rainfall–runoff models?

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.     

Hydrometeorological dataset (2014–2019) from the high Arctic unglaciated catchment Fuglebekken (Svalbard)

In this study, a hydrometeorological dataset of unglaciated High Arctic catchment is presented. The time series encompasses air temperature, precipitation, wind speed, relative humidity, and runoff data from 2014 to 2019. Meteorological data come from continuous meteorological monitoring carried out at the Hornsund station located in SW Spitsbergen. Flow in the Fuglebekken stream was measured using a portable flowmeter Nivus PCM-F with Active Doppler sensor. Continuous hydrometeorological monitoring in polar environments is crucial for the understanding processes controlling the water circulation in the catchments. Inter- and intra- annual variability of the provided variables gives an insight into river functioning. The data set is provided in an open-access PANGAEA repository (https://doi.pangaea.de/10.1594/PANGAEA.921921), in three time intervals (6, 12, and 24 hours). It may serve as the input to rainfall-runoff hydrological models, and allows multi-model parameter estimation and validation. It can be used in a variety of research topics, including streamflow projections, and more generally in examining Arctic ecosystems and climate change impact studies.     

On the importance of training methods and ensemble aggregation for runoff prediction by means of artificial neural networks

ABSTRACT

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     

Climate Change Impact on Hydrological Extremes: Preliminary Results from the Polish-Norwegian Project

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.     

Modelling of solute transport in rivers under different flow rates: A case study without transient storage

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.     

On the choice of calibration periods and objective functions: A practical guide to model parameter identification

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.     

Projected changes in flood indices in selected catchments in Poland in the 21st century

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.     

Climate changes in Central Europe projected by general circulation models

The paper is based on recent climate simulations provided by the leading world climate centres and available through the Data Distribution Centre of the Intergovernmental Panel on Climate Change. Seven models have been considered. Three experiments: control, and two integrations for different greenhouse gases emission scenarios have been analysed. Assessments have been made for the European window defined as 5–40° E, 40–60° N. The paper presents some selected results for the air surface temperature and precipitation. This is not a study on evaluation of models, detailed intercomparisons between models can be found in other works (e.g., Barnett, 1999; Boer and Lambert, 2000; Lambert and Boer, 2001), as well as on Internet (e.g., a web-site of the Program for Climate Model Diagnosis and Intercomparison¹). The intention of the authors was to show examples of analyses made specifically for the Central European region. For the reason of technical limitations of the paper only some results can be presented. This revised version was published online in July 2006 with corrections to the Cover Date.