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Evaporation is one of the most important parameters of meteorological science. Therefore, predicting evaporation is necessary for both water resources and planning management. The present study uses Bayesian Model Averaging (BMA) based on developed and optimized Kernel Extreme Learning Machine (KELM) models for predicting daily evaporation in different provinces of Iran with different climates. The Water Strider Algorithm, Salp Swarm Algorithm, Shark Algorithm, and Particle Swarm Optimization were combined with the KELM to predict daily evaporation in the Hormozgan, Mazandaran, Fars, Yazd, and Isfahan provinces. The models' inputs were average temperature, rainfall, number of sunny hours, wind speed, and relative humidity. The introducing a new hybrid gamma test for determining the adequate inputs, using hybrid and optimized KELM based on developing ELM for predicting evaporation, integrating individual models for predicting evaporation, and quantifying the uncertainty of outputs are the main innovations of the current study. Multiple error indices were used to evaluate the ability of models for predicting evaporation. The standalone and optimized KELM models were used to predict daily evaporation in the first level. In the next level, the BMA based on outputs of standalone and optimized KELM models predicted daily pan evaporation. The general results indicated that the BMA provided the best accuracy among other models in all stations. This study also introduced the new hybrid gamma test (GT-WSA) for choosing the best input combinations. The hybrid GT-WSA gave the best input combination without computing all input combinations (25 − 1). The uncertainty analysis of models also indicated that the uncertainty of BMA and optimized KELM models was lower than that of the KELM model. [ABSTRACT FROM AUTHOR]

The aim of the study was to establish the strength and direction of the relationship between daily temperature of river water and air with the use of selected estimation methods. The relationship was assessed for the River Noteć and its tributaries (Western Poland), using the cross-correlation function and Granger causality. The study established cause-and-effect relations for "water–air" and "air–water" directions of influence. It was confirmed that forecasting the pattern of flowing water temperature from changes in air temperature yields better results when done based on data from the previous day. Results of modelling the relationship between data series with the use of the linear and natural cubic splines models confirmed the presence of a nonlinear relation. It was also established that there is a statistically significant correlation of random fluctuations for both temperature series on the same days. This made it possible to confirm the occurrence of short-term connections between water and air temperature. The results can be used to determine the qualities of thermal regimes and to predict temperature of river waters in the conditions of climate change. [ABSTRACT FROM AUTHOR]

Assessments of the infiltration recharge of groundwater are performed using various methods and on different scales. Infiltration is dependent of climatic factors, aspects of water circulation, as well as on quasi-stationary and variable environmental features of a specific area, which are frequently difficult to determine on the basis of direct measurements or observations. The objective of the present study was to identify factors conditioning recharge of shallow groundwater in selected catchment areas of the Poznań Upland using the WetSpass simulation water balance model with spatially distributed parameters. Our analysis has indicated favourable and unfavourable conditions for recharge of groundwater in the annual period and in both half-year periods, which are the result of mutual relationships between the physical qualities of these catchment areas and their climatic and hydrological characteristics. The results obtained also confirmed the impact of surface runoff and actual evapotranspiration on the spatial distribution of effective infiltration. With soil types and groundwater depth distributions being similar in the catchment areas, changes in relationships between components of water balance are caused by differences in the type of land usage. Application of the WetSpass model has made it possible to arrive at a more accurate assessment of groundwater recharge. The results obtained may be used for erification of recharge areas and values of effective infiltration, set as a boundary condition in groundwater flow models. [ABSTRACT FROM AUTHOR]

The article presents the results of an analysis of the impact of cumulated series of negative air temperatures (cumulated degree-days) on the formation and persistence of the permanent ice cover on the River Noteć in the period 1987-2013. When assessing ice phenomena, use was made of observations conducted over 24-hour periods at four IMGW-PIB stations located along the River Noteć: Pakość (upper section), Ujście and Krzyż Wielkopolski (middle section) and Nowe Drezdenko (lower section). The authors determined the nature of the distribution of negative series and 24-hour air temperatures in terms of their duration, increase and distribution in years and months, using data from the Piła meteorological station. The primary objective was to specify the threshold values of cumulated degree-days at which the permanent ice cover starts to appear on the river, and the conditions under which this phenomenon is accelerated. When determining the strength of the relation between cumulated degree-days and the probability of appearance of an ice cover, use was made of the logistic regression model. Research showed that in the majority of instances ice cover appeared on the river at values of cumulated degree-days greater than: -16°C in Pakość, -21°C in Ujście and Krzyż Wielkopolski, and -73.5°C in Nowe Drezdenko. During the analysed period, 306 negative air temperature series occurred in the studief catchment area, with those lasting 5-10 days (76 of all instances) being predominant. As regards the River Noteć in Pakość, the logistic regression model confirmed that, on average, a one-degree increase in the value of cumulated degree-days is associated with an increase in the probability of appearance of the permanent ice cover of approximately 2.10%, of approximately 1.17% in Ujście, of 2.72% in Krzyż Wielkopolski, and of approximately 5.70% in Nowe Drezdenko. The more rapid the increase in cumulated series of negative air temperatures, the greater the probability of ice cover appearing and persisting on the River Noteć. The analysis pointed to the usefulness of the cumulated degree-days factor for predicting the appearance of ice cover on the river. The results obtained are significant for maintaining the economic and ecological function of the River Noteć. Information about the icing of the river has a practical dimension, among others as concerns identifying and minimising hazards connected with the occurrence of jams and ice-jam floods, which bring about enormous economic losses and constitute a threat to human life. [ABSTRACT FROM AUTHOR]

The aim of the study was the analysis of the integration of a flood risk management and water protection system in a selected section of the Warta Valley with taking into consideration the guidelines of the Water Framework Directive and the Floods Directive. The structure of the valley is comprised within a distance of 201.1 km by stretches of: an urstromtal, between the towns of Konin and Śrem, and the Warta gorge stretch between Śrem and Oborniki. High environmental values of the valley are shown, related to the presence of conservation areas and, on the other hand, a high flood hazard risk, resulting e.g. from advancing land development in the valley and its low retention capacity. The Integrated Water Resources Management guidelines conception was used in the analysis, inlcuding pointing out the directions of relations between minimizing flood risk and protection of natural values of the Warta River and its valley. Spatial distribution of the flood hazard and risk in the Warta Valley was also analyzed. A detailed analysis covered activities of a technical and non-technical character intended to stop the growth of flood risk. In the field of water conservation, the considered objectives included: achieving and maintaining a good ecological potential of waters or the risk of failing to achieve it, which is determined by a high urbanisation ratio, draining of an open-pit lignite mine, extensive morphological change in the river bed and pollution discharged into the waters from land used for agriculture. The results proved that one of the most significant factors directly impacting the environmental condition and ecological potential of the waters is the activity related to flood risk reduction. Potential threats to the functioning of NATURA 2000 areas characterized by high natural values were also indicated. [ABSTRACT FROM AUTHOR]

In the paper we present the results of shallow groundwater recharge estimation using the WetSpass GISbased distributed water balance model. By taking into account WetSpass, which stands for Water an Energy Transfer between Soil, Plants and Atmosphere under quasi-Steady State, for average conditions during the period 1961-2000, we assessed the spatial conditions of the groundwater infiltration recharge process of shallow circulation systems in the Poznan Plateau area (the Great Poland Lowland in western Poland), which is classified as a region with observed water deficits. For three temporal variants, i.e. year, winter and summer half-years, we determined using the geological infiltration method by about 5-10% on average, marginally by 20%. [ABSTRACT FROM AUTHOR]

In this study, a spatially distributed water balance model WetSpass was used to simulate long-term average annual and seasonal recharge of groundwater, surface runoff and actual evapotranspiration in the Mogilnica catchment, the Wielkopolska Lowland (western Poland). It is a region with the lowest precipitation and runoff in Poland. The simulated hydrological variables in the Mogilnica catchment are made on raster model (xy = 0.0625 km²). A GIS based, WetSpass is a model: Water and Energy Transfer between Soil, Plants and Atmosphere under quasi-Steady State. For water balance components estimations should be carried out concerning the meteorological conditions (precitipation, air temperature, wind speed, potential evapotranspiration), groundwater level and distributed topography, soil texture and land-use. In the Mogilnica catchment the long-term temporal and spatial average annual precipitation of 538 mm was distributed as: recharge of groundwater of 84 mm (15%), surface runoff of 31 mm (6%) and actual evapotranspiration of 425 mm (79%). Sustainable groundwater management and protection requires knowledge of spatial variation in water balance elements. [ABSTRACT FROM AUTHOR]

The temperature of river water (TRW) is an important factor in river ecosystem predictions. This study aims to compare two different types of numerical model for predicting daily TRW in the Warta River basin in Poland. The implemented models were of the stochastic type—Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA)—and the artificial intelligence (AI) type—Adaptive Neuro Fuzzy Inference System (ANFIS), Radial Basis Function (RBF) and Group Method of Data Handling (GMDH). The ANFIS and RBF models had the most fitted outputs and the AR, ARMA and ARIMA patterns were the most accurate ones. The results showed that both of the model types can significantly present suitable predictions. The stochastic models have somewhat less error with respect to both the highest and lowest TRW deciles than the AIs and were found to be better for prediction studies, with the GMDH complex model in some cases reaching Root Mean Square Error (RMSE) = 0.619 °C and Nash-Sutcliff coefficient (NS) = 0.992, while the AR(2) simple linear model with just two inputs was partially able to achieve better results (RMSE = 0.606 °C and NS = 0.994). Due to these promising outcomes, it is suggested that this work be extended to other catchment areas to extend and generalize the results. [ABSTRACT FROM AUTHOR]

This paper aims to find probabilities of extreme values of the air temperature for the Cerrado, Pantanal and Atlantic Forest biomes in Mato Grosso do Sul in Brazil. In this case a maximum likelihood estimation was employed for the probability distributions fitting the extreme monthly air temperatures for 2007–2018. Using the Extreme Value Theory approach this work estimates three probability distributions: the Generalized Distribution of Extreme Values (GEV), the Gumbel (GUM) and the Log-Normal (LN). The Kolmogorov–Smirnov test, the corrected Akaike criterion AICc, the Bayesian information criterion BIC, the root of the mean square error RMSE and the determination coefficient R2 were applied to measure the goodness-of-fit. The estimated distributions were used to calculate the probabilities of occurrence of maximum monthly air temperatures over 28–32 °C. Temperature predictions were done for the 2-, 5-, 10-, 30-, 50- and 100-year return periods. The GEV and GUM distributions are recommended to be used in the warmer months. In the coldest months, the LN distribution gave a better fit to a series of extreme air temperatures. Deforestation, combustion and extensive fires, and the related aerosol emissions contribute, alongside climate change, to the generation of extreme air temperatures in the studied biomes. [ABSTRACT FROM AUTHOR]

• A new hybrid model by coupling WT and ANN is developed to forecast water temperature in rivers. • Four mother wavelets (Daubechies, Symlet, discrete Meyer and Haar) are considered to develop the WT-ANN hybrid model. • The hybrid models perform much better than the regular ANN model in both normal and heat wave conditions. • The hybrid model with discrete Meyer mother wavelet performs the best, superior to the others. Accurate and reliable water temperature forecasting models can help in environmental impact assessment as well as in effective fisheries management in river systems. In this paper, a hybrid model that couples discrete wavelet transforms (WT) and artificial neural networks (ANN) is proposed for forecasting water temperature. Four mother wavelets, including Daubechies, Symlet, discrete Meyer and Haar, are considered to develop the WT-ANN hybrid model. The hybrid model is applied to forecast daily water temperature on the Warta River in Poland. Time series of daily water temperatures in eight river gauges as well as daily air temperatures of seven meteorological stations are used for forecasting daily water temperature. The performance of this WT-ANN hybrid model is evaluated by comparing the results with those obtained from linear and non-linear regression models as well as a traditional ANN model. The results show that the WT-ANN models perform well in simulating and forecasting river water temperature time series, and outperform the linear, non-linear and traditional ANN models. The superior performance of the WT-ANN models is particularly observed for extreme weather conditions, such as heat waves and drought. Among the four mother wavelets applied, the discrete Meyer performs the best, slightly better than the Daubechies at level 10 and Symlet, while the Haar mother wavelet has the lowest accuracy. In addition, the model performance improves with an increase in the decomposition level, indicating the importance of the choice of decomposition level. The outcomes of this study have important implications for water temperature forecasting and ecosystem management of rivers. [ABSTRACT FROM AUTHOR]

An analysis was conducted of the activity of individual homogeneous Hydrological Response Units (HRUs) and their impact on the components of the spatially distributed water balance based on the example of two urbanised catchments of the city of Poznań (Western Poland). Water balance was developed using the WetSpass model and GIS spatial data, based on hydrometeorological data from the reference period of 1961–2000 including projected land usage changes and precipitation changes expected by 2025 in the city. The catchments were parameterised with reference to land usage, soil permeability, terrain declivities and the level of groundwater waters in summer and winter. The dependence between HRUs and their impact on components of the water balance was determined. Water balance forecasts have shown two-way changes in the components of approximately 12% of the catchments. A significant increase of surface runoff (an increase of 20–30 mm/HRU) at the expense of effective infiltration reduction (by 15–20 mm/HRU) was determined for arable land intended for development. An increase of infiltration and evapotranspiration at the expense of the surface runoff reduction is forecast for areas designed for urban afforestation. The tendency of increase of atmospheric precipitation within the city until 2025 was indicated by changes in the water balance components. Changes in the landscape resulting from urban expansion may lead to detrimental hydrological effects: accumulation of surface runoffs and occurrence of local flash flooding, as confirmed by the simulations carried out using the WetSpass model. The results may contribute to a more accurate understanding of the impact of urban landscape modification patterns on the water balance at the regional and local scale. [ABSTRACT FROM AUTHOR]

The article presents the results of an analysis of the impact of cumulative negative air temperature degree-days (CDD) on formation of ice cover on the typical lowland river in the moderate climate zone (River Noteć, western Poland). During the period 1987–2013 the occurrence of a series of 306 consecutive days of negative air temperatures was identified in relation to atmospheric circulation in the winter seasons, with a duration of 1–53 days. The longest CDD series occurred in 2010, while low air temperatures were particularly frequent in the year 1996 (−600 °C·d). The analysis showed that this phenomenon was most identifiable in December. Research made it possible to distinguish two types of circulation, which were conducive to the cumulation of CDD. Using the logistic regression model it was determined that the probability of ice cover occurrence increased most rapidly in the lower course (−70 °C·d), while the weakest interdependence was observed along the other sections. On average, a one-degree-day increase of CDD was connected with an increase in the probability of a permanent ice cover appearing of approximately 1.2–5.7%. The results obtained are of particular significance for identifying the thermal and circulatory factors determining the appearance of ice cover on the rivers of the Polish Lowlands. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the teleconnection of large-scale climatic signals with river flow discharge in northern Iran. To this end, the Garmrud, Alishrud, and Babolrud rivers were selected: their monthly data set during the years 1993–2018 was received from the regional water company of the Mazandaran Province. Climate indices including the Southern Oscillation Index, extreme eastern tropical Pacific sea surface temperature (Niño 1 + 2), Global Mean Temperature Index, North Pacific pattern (NP), Pacific Decadal Oscillation, and North Atlantic Oscillation were investigated. The results of the cross-correlation, and the Spearman's test showed that there is a significant relationship at a 0.01 level between the indices and the river flow discharge. The most correlated indices were the NP and Niño 1 + 2. The NP index was inversely correlated and the Niño 1 + 2 was directly correlated with the rivers' flow variations, and the time lag of these two indices' maximum effectiveness was 2 months and 1 month, respectively. The adaptive neuro-fuzzy inference system and multilayer perceptron (MLP) models were used as the predictor models. The inputs were selected from the time lags of both river discharge and climatic indices. According to the normalized root mean square error (NRMSE) values (0.14–0.22), the predictions are evaluated as having moderate and appropriate qualities. The best performance of all models, R = 0.824 and RMSE = 0.350 CMS, was achieved for the MLP model. The results showed that consideration of the climatic indices as inputs may improve monthly flow predictability by an average of 24.1% (11.4–38.4% among the rivers and models). [ABSTRACT FROM AUTHOR]