•Rainfall partitioning is the most influenced by amount of rainfall and its intensity.•Drop diameter and velocity significantly influenced rainfall partitioning.•Affecting variables differ among ...vegetation periods and tree species.
Rainfall partitioning is an important part of the ecohydrological cycle, influenced by numerous variables. Rainfall partitioning for pine (Pinus nigra Arnold) and birch (Betula pendula Roth.) trees was measured from January 2014 to June 2017 in an urban area of Ljubljana, Slovenia. 180 events from more than three years of observations were analyzed, focusing on 13 meteorological variables, including the number of raindrops, their diameter, and velocity. Regression tree and boosted regression tree analyses were performed to evaluate the influence of the variables on rainfall interception loss, throughfall, and stemflow in different phenoseasons. The amount of rainfall was recognized as the most influential variable, followed by rainfall intensity and the number of raindrops. Higher rainfall amount, intensity, and the number of drops decreased percentage of rainfall interception loss. Rainfall amount and intensity were the most influential on interception loss by birch and pine trees during the leafed and leafless periods, respectively. Lower wind speed was found to increase throughfall, whereas wind direction had no significant influence. Consideration of drop size spectrum properties proved to be important, since the number of drops, drop diameter, and median volume diameter were often recognized as important influential variables.
Hydrological modelling can be complex in nonhomogeneous catchments with diverse geological, climatic, and topographic conditions. In this study, an integrated conceptual model including the snow ...module with machine learning modelling approaches was implemented for daily rainfall-runoff modelling in mostly karst Ljubljanica catchment, Slovenia, which has heterogeneous characteristics and is potentially exposed to extreme events that make the modelling process more challenging and crucial. In this regard, the conceptual model CemaNeige Génie Rural à 6 paramètres Journalier (CemaNeige GR6J) was combined with machine learning models, namely wavelet-based support vector regression (WSVR) and wavelet-based multivariate adaptive regression spline (WMARS) to enhance modelling performance. In this study, the performance of the models was comprehensively investigated, considering their ability to forecast daily extreme runoff. Although CemaNeige GR6J yielded a very good performance, it overestimated low flows. The WSVR and WMARS models yielded poorer performance than the conceptual and hybrid models. The hybrid model approach improved the performance of the machine learning models and the conceptual model by revealing the linkage between variables and runoff in the conceptual model, which provided more accurate results for extreme flows. Accordingly, the hybrid models improved the forecasting performance of the maximum flows up to 40 % and 61 %, and minimum flows up to 73 % and 72 % compared to the CemaNeige GR6J and stand-alone machine learning models. In this regard, the hybrid model approach can enhance the daily rainfall-runoff modelling performance in nonhomogeneous and karst catchments where the hydrological process can be more complicated.
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•Hybrid models integrating conceptual and machine learning models were implemented in the karst Ljubljanica River catchment.•The conceptual model CemaNeige GR6J yielded a very good performance, but it overestimated low flows.•The stand-alone machine learning models yielded poor performance compared to the conceptual and hybrid models.•The hybrid models outperformed the stand-alone models, particularly in simulating low and high flows.
•Time trend- and precipitation-informed models are tested.•31 gauging stations with 55 years of data were used.•Precipitation-informed models outperform time trend-informed models.•Tested models ...yielded significantly different flood quantiles.•The best-fitting model is to some extent related to the flow regime.
Estimation of reliable design discharges under variable climate is a key challenge for today’s engineers. Therefore, researchers are intensively exploring different alternative approaches in order to improve standard methods for design discharge estimation. Paper investigates the performance of time-invariant, time trend- and precipitation-informed models based on generalized extreme value (GEV) distribution for 31 Slovenian discharge gauging stations with data availability from 1961 until 2015. Different rainfall durations are used as covariates in the case of precipitation-informed models. The selected catchments are located in different climate regions and characterized by five flow regimes. The results indicate that in most cases precipitation-informed models gave better fit to the measured data comparing to time-invariant and time trend-informed models. Relative differences in the design discharge estimations associated with 10- and 100-year return periods using time trend- and precipitation-informed models compared to time-invariant model were up to 60%. Additionally, the results indicate that identified best-fitting model of individual gauging station can to some extent be related to its flow regime.
•Hydrological modelling of karst catchments is carried out.•The lumped GR4J model is the most suitable for the investigated catchments.•Decision tree model yields the worst results.•Inclusion of ...additional meteorological variables does not improve modelling results.•Low and high flows are additionally investigated.
Hydrological modelling is a challenging and significant issue, especially in nonhomogeneous catchments in terms of geology, and it is an essential part of water resources management. In this study, daily rainfall-runoff modelling was carried out using the lumped conceptual model, the artificial neural network (ANN), the deep-neural network (DNN), and regression tree (RT) data mining models for the nonhomogeneous karst Ljubljanica catchment and four of its sub-catchments in Slovenia with different geological characteristics. Model performance was evaluated using several performance criteria and additional investigation of low and high flows was carried out. The results of the study indicate that the Génie Rural à 4 paramètres Journalier (GR4J) lumped conceptual model yielded better modelling performance compared to the data-driven models, namely ANN, DNN and RT models. Moreover, the enhanced version of the GR4J model (i.e. GR6J) also yielded good performance in terms of the recession part. The RT model yielded the worst performance regarding runoff forecasting among the examined models in the case of all five investigated catchments. However, ANN and DNN data-driven models were slightly more successful in modelling the hydrograph recession in the case of karst sub-catchments compared to the GR4J lumped conceptual model structure. Inclusion of additional meteorological variables to ANN and DNN does not significantly improve modelling results.
•Selected extreme rainfall events in the last 25years in Slovenia were investigated.•Rainfall characteristics triggering flash floods and landslides are different.•Copulas yield useful ...intensity–duration–frequency relationship (IDF).•Rainfall inter-event time selection has significant influences on IDFs.•Different rainfall thresholds should apply in different parts of Slovenia.
Floods, landslides and debris flows are natural events that occur all over the world and are often induced by extreme rainfall conditions. Several extreme events occurred in Slovenia (Europe) in the last 25years that caused 18 casualties and approximately 500million Euros of economic loss. The intensity–duration–frequency (IDF) relationship was constructed using the Frank copula function for several rainfall stations using high-resolution rainfall data with an average subsample length of 34years. The empirical rainfall threshold curves were also evaluated for selected extreme events. Post-event analyses showed that rainfall characteristics triggering flash floods and landslides are different. The sensitivity analysis results indicate that the inter-event time definition (IETD) and subsample definition methodology can have a significant influence on the position of rainfall events in the intensity–duration space, the constructed IDF curves and on the relationship between the empirical rainfall threshold curves and the IDF curves constructed using the copula approach. Furthermore, a combination of several empirical rainfall thresholds with an appropriate high-density rainfall measurement network can be used as part of the early warning system of the initiation of landslides and debris flows. However, different rainfall threshold curves should be used for lowland and mountainous areas in Slovenia.
•Reference evapotranspiration trends in Slovenia were evaluated.•The calculated trends for different samples are mostly increasing.•No consistent trend could be detected for all considered ...stations.•Solar radiation was recognized as the most influential variable.•The contribution of radiative component is larger than of the aerodynamics component.
Evapotranspiration is an important element of the water cycle and possible trends in the evapotranspiration can, among others, influence on the water management and agricultural production. Since actual evapotranspiration values are rare and difficult to estimate, reference evapotranspiration was examined in order to document changes in the climatic conditions that affect evapotranspiration. The daily reference evapotranspiration data calculated using the Penman-Monteith method from 18 meteorological stations located in three different climate types in Slovenia was examined in this study. 55 years of data from 1961 until 2016 was analysed. The Mann-Kendall test was applied in order to detect trends in different samples that were defined based on the daily reference evapotranspiration data. Relationship between the evapotranspiration and influencing factors was also investigated using the generalized boosted regression trees model. The calculated trends for different samples are mostly increasing and statistically significant while no consistent trend could be detected for all 18 stations. The maximum increase in the daily reference evapotranspiration for the observed period was 0.5 mm and the maximum decrease −0.4 mm. Moreover, upward trend was detected for two mountain stations and downward for one sub-Mediterranean station. No uniform trend could be found for the stations located in the temperate continental climate. Furthermore, generalized boosted regression trees model indicated that solar radiation has the largest impact on the reference evapotranspiration values, generally followed by air temperature, saturation vapour pressure deficit and wind speed.
Hydrological modelling, essential for water resources management, can be very complex in karst catchments with different climatic and geologic characteristics. In this study, three combined ...conceptual models incorporating the snow module with machine learning models were used for hourly rainfall-runoff modelling in the mostly karst Ljubljanica River catchment, Slovenia. Wavelet-based Extreme Learning Machine (WELM) and Wavelet-based Regression Tree (WRT) machine learning models were integrated into the conceptual CemaNeige Génie Rural à 4 paramètres Horaires (CemaNeige GR4H). In this regard, the performance of the hybrid models was compared with stand-alone conceptual and machine learning models. The stand-alone WELM and WRT models using only meteorological variables performed poorly for hourly runoff forecasting. The CemaNeige GR4H model as stand-alone model yielded good performance; however, it overestimated low flows. The hybrid CemaNeige GR4H-WELM and CemaNeige-WRT models provided better simulation results than the stand-alone models, especially regarding the extreme flows. The results of the study demonstrated that using different variables from the conceptual model, including the snow module, in the machine learning models as input data can significantly affect the performance of rainfall-runoff modelling. The hybrid modelling approach can potentially improve runoff simulation performance in karst catchments with diversified geological formations where the rainfall-runoff process is more complex.
•Drop-size distribution measurements below and above tree canopy were conducted.•Effect of birch tree on the soil erosion related processes was analysed.•A decrease in the raindrops erosivity below ...the canopy was demonstrated.•Impact of leafed and leafless period on the rainfall erosivity was significant.
Healthy soils are one of the key priorities of the EU Soil Strategy, and soil erosion can present a threat to soils around the world. Rainfall erosivity is the main driver of soil erosion by water. Rainfall interception by vegetation can reduce the erosive power of raindrops, and consequently measures involving vegetation can mitigate soil erosion losses. In this study, the effect of rainfall interception on the erosive power of raindrops under the birch tree in an urban park in the city of Ljubljana is investigated. More than one year of measurements of drop-size distribution using two optical disdrometers placed above and below the birch tree canopy were used to investigate the impact of rainfall interception on the erosive power of raindrops. The number of drops, fall velocity, and drop diameter were, on average smaller below the canopy in comparison to the measurements above the canopy for 20%, 7% and 27%, respectively. This also resulted in a reduction in the rainfall kinetic energy (3% and 30% in the leafless and leafed periods, respectively) and rainfall erosivity (21% and 50% for the leafless and leafed periods, respectively). The results demonstrate that rainfall interception has a significant seasonal influence on the erosive power of raindrops. Therefore, vegetation characteristics should be considered as time-varying rather than constant parameters in soil erosion modelling studies.
•Investigated the rainfall intensity below an urban tree canopy using disdrometer data.•Leafed birch tree contributed to more significant reduction in rainfall intensity.•Higher intensity reduction ...is observed below an urban tree due to larger crown volume.•Vapor pressure deficit exerts more influence on throughfall intensity among atmospheric variables.•The intensity-reducing benefit of birch tree has a significant effect on runoff peak water level.
Trees have an indispensable role to play in the hydrological cycle. The process of interception by tree canopies alters the magnitude, pathway, and intensity of rainfall reaching the ground. This study investigates the rainfall intensity-attenuating effects of canopy interception by open-grown birch trees (Betula pendula Roth.) in an urban environment and the influence of atmospheric variables. Rainfall partitioning was measured in a research plot in the city of Ljubljana, Slovenia, from August 2021 to August 2022. Simultaneously, optical disdrometers above and below the birch tree canopy measured microstructures of rainfall and throughfall, from which the intensities were calculated. During the measurement period, the birch tree intercepted on average 25.6 % of gross rainfall, with the interception being twice as high during the leafed season than in the leafless season. Consequently, the total number and volume of drops under the canopy were reduced on average by 16.4 % and 48.7 %, respectively, indicating the interception and fragmentation of raindrops by the canopy. Owing to these processes, the leafed and leafless states of the birch tree canopy attenuate the average intensities of rainfall by 50.2 % and 41.6 %, respectively. Canopy interception also moderates the maximum 10-minute rainfall intensities by 11.6–83.8 % and 13.1–74.2 % during the leafed and leafless periods, respectively. This percentage of reduction in the rainfall intensities below the canopy decreases with rainfall amount and in the absence of foliage. Aside from phenoseasons, we also found that vapor pressure deficit and air temperature were among the atmospheric variables that exert the highest influence on the intensities of throughfall. Furthermore, the regression analysis between the maximum throughfall intensity and peak water level for each rainfall event indicates that the reduction of rainfall intensity by the canopy has a significant effect on runoff peak water level (R2 = 0.76, p < 0.001).
Groundwater represents a vast and highly important source of drinking water. As a part of the hydrological cycle, it can only be managed by understanding the components that make up its vulnerability ...profile. These are referenced with anthropogenic influences related to pressures connected with land use and the ever-increasing conversion of land surface for urban use. The quality of groundwater can be assessed and expressed using a number of different chemical parameters. In this paper, we focus on the analysis of groundwater’s physico-chemical field parameters and the major ions present in Slovenian aquifers. Several statistical methods were applied to outline the relevant criteria involved in determining the groundwater’s natural background. Additionally, a graphical method was applied to evaluate the source of major ions distribution in the groundwater. The most common BRIDGE methodology used to determine background values is based on upper percentile values. It turns out that this methodology might be relevant for chemical parameters mainly affected by geogenic sources, while the “anthropogenic” parameters have to be treated with a different approach e.g., probability plot method.
