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Climate change is expected to affect the frequency and magnitude of floods, which are among the costliest hazards in Europe. As natural hazards have a significant impact on infrastructure and people's lives and their habitats, novel measures to cope with climate change need to be considered. Different types of measures, such as green, grey and hybrid solutions, can be used to mitigate the impacts of natural hazards. Green measures (also referred to as nature-based solutions) are currently being promoted in the European Union, but several barriers to implementing these measures exist. The question arises as to what hinders the wider implementation of green measures and therefore results in a preference for conventional grey measures in some countries. This study examines the differences in the perceived effectiveness, feasibility and acceptance of different types of flood risk management measures in three European countries (Slovenia, Czechia, and the Netherlands). The results show statistically significant differences in the perceived effectiveness, feasibility and acceptance of the studied measures. With respect to individual measures, respondents in all three countries tend to view conventional grey measures (dams and cisterns) as more effective and acceptable than green and hybrid measures. However, grey measures are perceived as difficult to implement. The results reveal that the perceived effectiveness and acceptability of the measures are related. The major drivers affecting the differences in the perceptions of different measures are the countries of the respondents and the sociodemographic variables of income and age. In contrast to other studies, experiences with past floods and private insurance are not statistically significant. Our results thus indicate that, along with individual behaviour, aggregate social drivers should be considered when implementing flood risk management measures across the EU.

Abstract During the next few decades, changes in rainfall frequency and magnitude are expected to have major impacts on landscape evolution, social, and economic aspects of human society. We focus on seasonal rainfall variations by the end of the twenty-first century to define affected landslide-prone areas, future landslide alerts and the impact of landslides on landscape development in the juncture of the Alpine, Pannonian, and Mediterranean region. A moderate and a worst-case climate scenario from CMIP5 global climate simulations were considered to determine the impact of rainfall on the two most common types of landslides in region, shallow and deep-seated landslides. The observed changes in the occurrence of shallow landslides are significant, especially in the winter months, where we can expect more landslide-prone areas compared to the baseline period. Shallow landslides will have a greater impact on the landscape in spring and summer than deep-seated landslides, especially in vineyards.

Soil erosion is a complex process involving multiple natural and anthropic agents, causing the deterioration of multiple components comprising soil health. Here, we provide an estimate of the spatial patterns of cropland susceptibility to erosion by sheet and rill, gully, wind, tillage, and root crops harvesting and report the co-occurrence of these processes using a multi-model approach. In addition, to give a global overview of potential future changes, we identify the locations where these multiple concurrent soil erosion processes may be expected to intersect with projected dry/wet climate changes by 2070. Of a modelled 1.48 billion hectares (B ha) of global cropland, our results indicate that 0.56 B ha (∼36% of the total area) are highly susceptible (classes 4 and 5) to a single erosion process, 0.27 B ha (∼18% of the total area) to two processes and 0.02 B ha (1.4% of the total area) to three or more processes. An estimated 0.82 B ha of croplands are susceptible to possible increases in water (0.68 B ha) and wind (0.14 B ha) erosion. We contend that the presented set of estimates represents a basis for enhancing our foundational knowledge on the geography of soil erosion at the global scale. The generated insight on multiple erosion processes can be a useful starting point for decision-makers working with ex-post and ex-ante policy evaluation of the UN Sustainable Development Goal 15 (Life on Land) activities. Scientifically, this work provides the hitherto most comprehensive assessment of soil erosion risks at the global scale, based on state-of-the-art models.

Abstract As a network of researchers we release an open-access database (EUSEDcollab) of water discharge and suspended sediment yield time series records collected in small to medium sized catchments in Europe. EUSEDcollab is compiled to overcome the scarcity of open-access data at relevant spatial scales for studies on runoff, soil loss by water erosion and sediment delivery. Multi-source measurement data from numerous researchers and institutions were harmonised into a common time series and metadata structure. Data reuse is facilitated through accompanying metadata descriptors providing background technical information for each monitoring station setup. Across ten European countries, EUSEDcollab covers over 1600 catchment years of data from 245 catchments at event (11 catchments), daily (22 catchments) and monthly (212 catchments) temporal resolution, and is unique in its focus on small to medium catchment drainage areas (median = 43 km2, min = 0.04 km2, max = 817 km2) with applicability for soil erosion research. We release this database with the aim of uniting people, knowledge and data through the European Union Soil Observatory (EUSO).

Here, we present and release the Global Rainfall Erosivity Database (GloREDa), a multi-source platform containing rainfall erosivity values for almost 4000 stations globally. The database was compiled through a global collaboration between a network of researchers, meteorological services and environmental organisations from 65 countries. GloREDa is the first open access database of rainfall erosivity (R-factor) based on hourly and sub-hourly rainfall records at a global scale. This database is now stored and accessible for download in the long-term European Soil Data Centre (ESDAC) repository of the European Commission's Joint Research Centre. This will ensure the further development of the database with insertions of new records, maintenance of the data and provision of a helpdesk.In addition to the annual erosivity data, this release also includes the mean monthly erosivity data for 94% of the GloREDa stations. Based on these mean monthly R-factor values, we predict the global monthly erosivity datasets at 1 km resolution using the ensemble machine learning approach (ML) as implemented in the mlr package for R. The produced monthly raster data (GeoTIFF format) may be useful for soil erosion prediction modelling, sediment distribution analysis, climate change predictions, flood, and natural disaster assessments and can be valuable inputs for Land and Earth Systems modelling.

The Sava River Basin (SRB) includes six countries (Slovenia, Croatia, Bosnia and Herzegovina, Serbia, Albania, and Montenegro), with the Sava River (SR) being a major tributary of the Danube River. The SR originates in the mountains (European Alps) of Slovenia and, because of a recent Slovenian government initiative to increase clean, sustainable energy, multiple hydropower facilities have been constructed within the past ~20 years. Given the importance of this river system for varying demands, including hydropower (energy production), information about past (paleo) dry (drought) and wet (pluvial) periods would provide important information to water managers and planners. Recent research applying traditional regression techniques and methods developed skillful reconstructions of seasonal (April–May–June–July–August–September or AMJJAS) streamflow using tree-ring-based proxies. The current research intends to expand upon these recent research efforts and investigate developing reconstructions of seasonal (AMJJAS) precipitation applying novel Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) techniques. When comparing the reconstructed AMJJAS precipitation datasets, the AI/ML/DL techniques statistically outperformed traditional regression techniques. When comparing the SRB AMJJAS precipitation reconstruction developed in this research to the SRB AMJJAS streamflow reconstruction developed in previous research, the temporal variability of the two reconstructions compared favorably. However, pluvial magnitudes of extreme periods differed, while drought magnitudes of extreme periods were similar, confirming drought is likely better captured in tree-ring-based proxy reconstructions of hydrologic variables.

Climate change has increasing impacts of hydro-meteorological extremes on water resources. Projections indicate a similar trend and challenge in the effectiveness of conventional engineering solutions in climate change adaptation (CCA) and disaster risk reduction (DRR) strategies. Nature-based solutions (NbSs) have been promoted as viable approaches and measures that complement engineering solutions. While the effects of NbSs have been increasingly demonstrated, their broader implementation can be favoured by NbSs knowledge integration in higher education curricula. Knowledge on how the research practice is translated into the educational landscape is missing. This paper adopts the concept of knowledge translation and analyses the integration of NbSs in the study programs of higher education institutions in three European countries (Croatia, Czechia, and Slovenia). Specifically, it explores the extent, thematic areas, and curricular settings of NbSs related to water resources management in implemented curricula at public universities. The results show that NbSs are integrated in a limited number of courses within the relevant study programs (in the fields of, e.g., natural sciences, geography, and engineering and technology) and represent rather an extension of compulsory curricula. Bibliometric analysis revealed that most courses involving the NbSs approach still represent a personalized knowledge, i.e., developed by professors during their research activities. The barriers impairing a broader integration of NbSs in the studied programs are then discussed. Our results therefore indicate that NbSs do not represent a mainstream knowledge that would proliferate into higher education curricula through accreditations procedures, but that the knowledge that is mostly integrated through direct incremental implementation of NbSs in the individual compulsory lessons or facultative courses. We assert that without broader and systematic NbSs knowledge translation to study programs, the effectiveness of climate change adaptation and disaster risk reduction strategies cannot be fully achieved.

Different sediment-related disasters due to torrential hazards, such as flash floods, debris flows, and landslides, can occur in an Alpine torrential catchment. When protecting infrastructure and human lives, different structural and non-structural protection measures can be used to mitigate permanent and future risks. An overview of the mitigation measures constructed near the Krvavec ski resort in northwest Slovenia (Central Europe) is presented. In May 2018, an extreme debris flood occurred in this area, causing significant economic damage. After the May 2018 event, different field investigations (i.e., geological and topographic surveys) and modeling applications (e.g., hydrological modeling, debris flow) have been conducted with the purpose of preparing the required input data for the design of protection measures against such disasters in future—due to climate change, more disasters are expected to happen in this torrential watershed. The mitigation includes the restoration of local streams, the construction of a large slit check dam for sediment retention, the construction of several smaller check dams and the construction of 16 flexible net barriers with an estimated ~8000 m3 retention volume for controlling in-channel erosion in steep torrential streams. Additionally, in order to observe and monitor potential future extreme events, an extensive monitoring system has been established in the investigated area. This monitoring system will cover measurements of flexible net corrosion, the estimation of concrete abrasion at check dams, periodical geodetic surveys using small drones (UAV), hydro-meteorological measurements using rainfall gauges and water level sensors. The recent extreme floods of August 2023 also hit this part of Slovenia, and this combination of technical countermeasures withstood the event and prevented large amounts of coarse debris from being transported to the downstream section and devastating infrastructure, as was the case in May 2018 during a less extreme event. Therefore, such mitigation measures can also be used in other torrential catchments in the Alpine environment.

The Sava River Basin (SRB) extends across six countries (Slovenia, Croatia, Bosnia and Herzegovina, Serbia, Albania, and Montenegro) and is a major tributary of the Danube River (DR). The Sava River (SR) originates in the alpine region of Slovenia, and, in support of a Slovenian government initiative to increase clean, sustainable energy, multiple hydropower facilities have been constructed within the past ~20 years. Given the importance of this river system for varying demands, including energy production, information about past (paleo) drought and pluvial periods would provide important information to water managers and planners. Seasonal (April–May–June–July–August–September—AMJJAS) streamflow data were obtained for two SRB gauges (Jesenice and Catez) in Slovenia. The Jesenice gauge is in the extreme headwaters of the SR, upstream of any major water control structures, and is considered an unimpaired (minimal anthropogenic influence) gauge. The Catez gauge is located on the SR near the Slovenia–Croatia border, thus providing an estimate of streamflow leaving Slovenia (entering Croatia). The Old World Drought Atlas (OWDA) provides an annual June–July–August (JJA) self-calibrating Palmer Drought Severity Index (scPDSI) derived from 106 tree-ring chronologies for 5414 grid points across Europe from 0 to 2012 AD. In lieu of tree-ring chronologies, this dataset was used as a proxy to reconstruct (for ~2000 years) seasonal streamflow. Prescreening methods included the correlation and temporal stability of seasonal streamflow and scPDSI cells. The retained scPDSI cells were then used as predictors (independent variables) to reconstruct streamflow (predictive and/or dependent variables) in regression-based models. This resulted in highly skillful reconstructions of SRB seasonal streamflow from 0 to 2012 AD. The reconstructions were evaluated, and both low flow (i.e., drought) and high flow (i.e., pluvial) periods were identified for various filters (5-year to 30-year). When evaluating the most recent ~20 years (2000 to present), multiple low-flow (drought) periods were identified. For various filters (5-year to 15-year), the 2003 end-year consistently ranked as one of the lowest periods, while the 21-year period ending in 2012 was the lowest flow period in the ~2000-year reconstructed-observed-historic period of record. The ~30-year period ending in 2020 was the lowest flow period since the early 6th century. A decrease in pluvial (wet) periods was identified in the observed-historic record when compared to the paleo record, again confirming an apparent decline in streamflow. Given the increased activities (construction of water control structures) impacting the Sava River, the results provide important information to water managers and planners.

This paper describes the data gathered for a paper published in Earth-Science Reviews (DOI: 10.1016/j.earscirev.2023.104339) to address the problem of studies using incorrect equations to calculate rainfall erosivity (R factor), which can lead to issues related to land degradation, soil productivity loss, and biodiversity loss. The aim was to locate articles containing the incorrect equations and create a relational database that could be used to perform an in-depth analysis of the errors. Because the search target is an equation, it is impossible to directly query any literature database for the articles that contain the incorrect R equations. Therefore, a manual search of multiple databases was conducted. Subsequently, the literature search was broadened to identify the origin of the misuse of the R equations, and SQL (Structured Query Language) queries were formulated to understand why the errors continued to persist for a minimum of 33 years. The resulting entity-relationship-based Microsoft Access database was determined to be a valuable tool for performing in-depth analysis. It can be used to add incorrect studies and perform further analysis. It is suggested that further research should be conducted to determine the extent of the impact of these errors on soil erosion, ecosystems, and the environment.

Compound events occur when multiple drivers or hazards occur in the same region or on the same time scale, hence amplifying their impacts. Compound events can cause large economic damage or endanger human lives. Thus, a better understanding of the characteristics of these events is needed in order to protect human lives. This study investigates the drivers and characteristics of floods in Europe and North America from the compound event perspective. More than 100 catchments across Europe and North America were selected as case study examples in order to investigate characteristics of floods during a 1979–2019 period. Air temperature, precipitation, snow thickness, snow liquid water equivalent, wind speed, vapour pressure, and soil moisture content were used as potential drivers. Annual maximum floods were classified into several flood types. Predefined flood types were snowmelt floods, rain-on-snow floods, short precipitation floods and long precipitation floods that were further classified into two sub-categories (i.e., wet and dry initial conditions). The results of this study show that snowmelt floods were often the dominant flood type in the selected catchments, especially at higher latitudes. Moreover, snow-related floods were slightly less frequent for high altitude catchments compared to low- and medium-elevation catchments. These high-altitude areas often experience intense summer rainstorms that generate the highest annual discharges. On the other hand, snowmelt-driven floods were the predominant flood type for the lower elevation catchments. Moreover, wet initial conditions were more frequent than the dry initial conditions, indicating the importance of the soil moisture for flood generation. Hence, these findings can be used for flood risk management and modelling.

Rainfall erosivity is one of the most important parameters that influence soil erosion rates. It is characterized by a large spatial and temporal variability. For example, in Slovenia, which covers around 20,000 km2, the annual rainfall erosivity ranges from less than 1,000 MJ mm ha−1 h−1 to more than 10,000 MJ mm ha−1 h−1. Drop size distribution (DSD) data are needed to investigate rainfall erosivity characteristics. More than 2 years of DSD measurements using optical disdrometers located at six stations in Slovenia were used to investigate the spatial and temporal variability in rainfall erosivity in Slovenia. Experimental results have indicated that elevation is a poor predictor of rainfall erosivity and that erosivity is more strongly correlated to the mean annual precipitation. Approximately 90% of the total kinetic energy (KE) was accounted for in about 35% of 1 min disdrometer data. The highest 1 min intensities (I) and consequently also KE values were measured in summer followed by autumn and spring. The local KE-I equation yielded an acceptable fit to the measured data in case of all six stations. The relatively large percentage of 1 min rainfall intensities above 5 mm/h can at least partially explain some very high annual rainfall erosivity values (i.e., near or above 10,000 MJ mm ha−1 h−1). Convective and large-scale precipitation events also result in various rainfall erosivity characteristics. The station microlocation and wind impacts in case of some stations yielded relatively large differences between the data measured using the optical disdrometer and the pluviograph. Preliminary conclusions have been gathered, but further measurements are needed to get even better insight into spatial and temporal variability in rainfall erosivity under Alpine climate in Slovenia.

An adequate representation of the relationship between effective rainfall and rainfall losses is required in hydrological rainfall–runoff models to reduce the uncertainty of the modelling results. This study evaluates the performance of several hydrological rainfall loss methods using the experimental data obtained from a laboratory small-scale physical landslide model with variable slope inclination, homogenous material and no vegetation effects. Three different experiments were selected and five rainfall loss methods were tested to evaluate their performance in reproducing the experimental results from the perspective of the surface runoff formation on the experimental slope. Initial and calibrated parameters were used to test the performance of these hydrological rainfall loss methods. The results indicate that the initial parameters of the rainfall loss model can satisfactorily reproduce the experimental results in some cases. Despite the fact that the slope material characteristics used in the laboratory experiments were relatively homogenous, some well-known methods yielded inaccurate results. Hence, calibration of the rainfall loss model proved to be essential. It should also be noted that, in some cases, the calibrated model parameters were relatively different from the initial model parameters estimated from the literature. None of the tested hydrological rainfall loss methods proved to be superior to the others. Therefore, in the case of natural environments with heterogeneous soil characteristics, multiple rainfall loss methods should be tested and the most suitable method should be selected only after cross-validation or a similar evaluation of the tested methods.

Morphodynamic changes in the riverbed may be accelerated by the climate change-induced effects, mostly through the increase of the frequency of extreme climatic events such as floods. This can lead to scouring of the riverbed around the bridge substructure and consequently reduces its overall stability. In order to better understand hydromorphological processes at the local scale, the influence of floods on bridge scour requires a detailed analysis of several interacting flood hydrograph characteristics. This paper presents a multivariate analysis of the annual maximum (AM) flood discharge data at four gauging stations on the Drava and Sava Rivers in Croatia (Europe). As part of the hydrograph analysis, multiple baseflow separation methods were tested. Flood volumes and durations were derived after extracting the baseflow from measured discharge data. Suitable marginal distribution functions were fitted to the peak discharge (Q), flood volume (V) and duration (D) data. Bivariate copula analyses were conducted for the next pairs: peak discharge and volume (Q–V), hydrograph volume and duration (V–D) and peak discharge and hydrograph duration (Q–D). The results of the bivariate copula analyses were used to derive joint return periods for different flood variable combinations, which may serve as a preliminary analysis for the pilot bridges of the R3PEAT project where the aim is to investigate the influences on the riverbed erosion around bridges with installed scour countermeasures. Hence, a design hydrograph was derived that could be used as input data in the hydraulic model for the investigation of the bridge scour dynamics within the project and a preliminary methodology is proposed to be applied. The results indicate that bivariate frequency analysis can be very sensitive to the selected baseflow separation methodology. Therefore, future studies should test multiple baseflow separation methods and visually inspect the performance.

Detecting the mechanism of landslides and evaluating their dynamics is challenging, especially concerning composite landslides. For this purpose, several investigation and monitoring techniques should be implemented to obtain reliable information on landslide characteristics (e.g., geological and hydrogeological conditions and type of landslide processes), kinematics (displacement rate), and potential triggering mechanisms (e.g., change in groundwater table and precipitation). The Urbas landslide in northwest Slovenia has been studied for decades through geological, geotechnical, geodetic, and remote sensing investigations. However, due to the complexity of the landslide and the short duration of continuous monitoring, no assessment of its dynamics has been made. To meet this need, this study analysed continuous and periodic monitoring of landslide displacements using data from the global navigation satellite system (GNSS), a wire extensometer, unmanned aerial vehicle (UAV) photogrammetry, and hydrometeorological sensing (groundwater table, precipitation). The results of this study show that the dynamics of the Urbas landslide differ along the landslide area, depending on local geological and hydrogeological conditions. Consequently, certain parts of the landslide are at different evolutionary states and respond differently to the same external triggers.

Debris flows are among the natural hazards that can occur in mountainous areas and endanger people’s lives and cause large economic damage. Debris flow modelling is needed in multiple applications such as design of protection measures or preparation of debris flow risk maps. Many models are available that can be used for debris flow modelling. The Rapid Mass Movement Simulation (RAMMS) model with its debris flow module, (i.e. RAMMS-DF) is one of the most commonly used ones. This review provides a comprehensive overview of past debris flow modelling applications in an alpine environment with their main characteristics, including study location, debris flow magnitude, simulation resolution, and Voellmy-fluid friction model parameter ranges, (i.e. μ and ξ). A short overview of each study is provided. Based on the review conducted, it is clear that RAMMS parameter ranges are relatively wide. Furthermore, model calibration using debris-flow post-event survey field data is the essential step that should be done before applying the model. However, an overview of the parameters can help to limit the parameter ranges. Particularly when considering the similarity between relevant case studies conducted in similar environments. This is especially relevant should the model be applied for estimating debris-flow hazard for potential future events. This model has been used mostly in Europe, (i.e. Alpine region) for modelling small and extremely large debris flows.

Landslides are one of the most frequent natural disasters that can endanger human lives and property. Therefore, prediction of landslides is essential to reduce economic damage and save human lives. Numerous methods have been developed for the prediction of landslides triggering, ranging from simple methods that include empirical rainfall thresholds, to more complex ones that use sophisticated physically- or conceptually-based models. Reanalysis of soil moisture data could be one option to improve landslide forecasting accuracy. This study used the publicly available FraneItalia database hat contains almost 9000 landslide events that occurred in the 2010–2017 period in Italy. The Copernicus Uncertainties in Ensembles of Regional Reanalyses (UERRA) dataset was used to obtain precipitation and volumetric soil moisture data. The results of this study indicated that precipitation information is still a much better predictor of landslides triggering compared to the reanalyzed (i.e., not very detailed) soil moisture data. This conclusion is valid both for local (i.e., grid) and regional (i.e., catchment-based) scales. Additionally, at the regional scale, soil moisture data can only predict a few landslide events (i.e., on average around one) that are not otherwise predicted by the simple empirical rainfall threshold approach; however, this approach on average, predicted around 18 events (i.e., 55% of all events). Despite this, additional investigation is needed using other (more complete) landslide databases and other (more detailed) soil moisture products.

Compound extreme weather events can cause large economic damage and endanger human lives. Therefore, identification of changes in such compound event frequency and magnitude is important information that could be useful for decision makers and practitioners in water management and agriculture sector. This is especially the case for dry hazards that can be significantly influenced by the increasing air temperature and can have significant impact on water availability and consumption as well as on agricultural production. This study investigated changes in the compound occurrence of drought and extreme heat at the European scale using Uncertainties in Ensembles of Regional Reanalyses (UERRA) regional reanalysis data for the 1961–2018 period. The effective drought index (EDI) and the air temperature percentile threshold were used for the identification of the compound events at the catchment scale where entire Europe was divided into more than 4000 catchments. The results revealed multiple hotspots of compound drought and extreme heat events such as parts of Western Europe, Italy, Balkan Peninsula and Northern and Eastern Europe. At the continental scale, no uniform trend pattern could be detected. However, multiple areas with either positive or negative changes were identified. A positive change was characteristic for parts of Western Europe, Italy, Balkan Peninsula, etc. In these cases, the trend was mostly driven by the decreasing total precipitation trend and was not directly affected by the increasing air temperature trend. Areas with negative changes include parts of Northern and Eastern Europe and British Isles. In these cases, the detected trend was mostly driven by an increasing total precipitation trend. However, local drivers could be different.

Conceptual rainfall-runoff models besides precipitation and discharge data generally require estimates of the mean daily air temperature as input data. For the estimation of the mean daily air temperature, there are different methods available. The paper presents an evaluation of the impact of the mean daily air temperature calculation on the rainfall-runoff modelling results. Additionally, other measured variables and rating curve uncertainty were assessed. Differences in the mean daily air temperature values were evaluated for the 33 meteorological stations in Slovenia and additional investigations were conducted for four selected meso-scale catchments located in different climates. The results of the application of four equations for the mean air temperature calculation yielded the mean absolute error values between 0.56–0.80 °C. However, the results of rainfall-runoff modelling showed that these differences had an almost negligible impact on the model results. Differences in the mean simulated discharge values were no larger than 1%, while differences in the maximum discharge values were a bit larger, but did not exceed 5%. A somewhat larger impact on the model results was observed when precipitation and water level measurements’ uncertainty was included. However, among all analysed input data uncertainties, the rating curve uncertainty can be regarded as the most influential with differences in the simulated mean discharge values in the range of 3% and differences in the maximum discharge values up to 14%.

Understanding the temporal variability of the nutrient transport from catchments is essential for planning nutrient loss reduction measures related to land use and climate change. Moreover, observations and analysis of nutrient dynamics in streams draining undisturbed catchments are known to represent a reference point by which human-influenced catchments can be compared. In this paper, temporal dynamics of nitrate-nitrogen (NO3-N) flux are investigated on an event basis by analysing observed lag times between data series. More specifically, we studied lag times between the centres of mass of six hydrological and biogeochemical variables, namely discharge, soil moisture at three depths, NO3-N flux, and the precipitation hyetograph centre of mass. Data obtained by high-frequency measurements (20 min time step) from 29 events were analysed. Linear regression and multiple linear regression (MLR) were used to identify relationships between lag times of the above-mentioned processes. We found that discharge lag time (LAGQ) and NO3-N flux lag time (LAGN) are highly correlated indicating similar temporal response to rainfall. Moreover, relatively high correlation between LAGN and soil moisture lag times was also detected. The MLR model showed that the most descriptive variable for both LAGN and LAGQ is amount of precipitation. For LAGN, the change of the soil moisture in the upper two layers was also significant, suggesting that the lag times indicate the primarily role of the forest soils as the main source of the NO3-N flux, whereas the precipitation amount and the runoff formation through the forest soils are the main controlling mechanisms.

Rain-on-snow (ROS) floods can cause economic damage and endanger human lives due to the compound effect of rainfall and snowmelt, especially under climate change. In this study, possible future changes of seasonality, magnitude and frequency characteristics of ROS floods were investigated for the selected catchments in Slovenia, Europe. For this purpose, five global/regional climate models (GCM/RCM) combinations were applied using the RCP4.5 climate scenario for the period 1981−2100. To determine ROS floods’ characteristics in the future, a lumped conceptual hydrological model Génie Rural à 6 paramètres Journalier (GR6J) with snow module CemaNeige was applied. The results indicate that the number of ROS floods could increase in the future. Moreover, also the magnitudes of extreme ROS floods could increase, while a slight decrease in the median values of ROS flood magnitudes was observed. The strength of seasonality for a high-altitude catchment could decrease in the future. A slight shift in the average ROS floods’ timing could be expected. Furthermore, a catchment located in a temperate continental climate could have a different response to the climate change impact in comparison to a catchment located in a mountain climate with alpine characteristics. Additionally, differences among investigated climate models show a large variability.

Rainfall-runoff modeling is nowadays applied for water resources management, water system design, real-time forecasting, flood design and can be carried out by using different types of hydrological models. In this study, we focused on lumped conceptual hydrological models and their performance in diverse sub-catchments of the Sava River in Slovenia, related to their size and non-homogeneity. We evaluated the difference between modeled and measured discharges of selected discharge gauging stations, using different model performance criteria that are usually applied in hydrology, connecting the results to geospatial analysis of geological and hydrogeological characteristics, land use, runoff potential, proportion of agglomeration and various meteorological variables. Better model performance was obtained for catchments with a higher runoff potential and with less variations in meteorological variables. Regarding the number of used parameters, the results indicated that the tested Genie Rural 6-parameter Journalier (GR6J) model with 6 parameters performed better than the Genie Rural 4-parameter Journalier (GR4J) model with 4 parameters, especially in the case of larger sub-catchments. These results illustrate the comprehensive nature of lumped models. Thus, they yield good performance in case of the catchments with indistinguishable characteristics.

The article presents the results of precipitation and aerosol (PM10) data analyses in Slovenia. Analyses of rainfall data for some areas of Slovenia, such as the Ljubljana Basin, Zasavje, Šalek Valley, Celje Basin, and the coastal area, suggest that rain on the weekend is more frequent than on weekdays; nevertheless, these deviations are not statistically significant. A comparison of three separate decades showed that the pattern of weekly rainfall in the 21st century is different than that in the last period of the 20th century. The weekly cycle of PM10 for the considered stations is quite similar to that of some of the more contaminated regions of the world. Furthermore, the connection between PM10 and precipitation in most of the analyzed cases was statistically significant.

High-frequency rainfall data is needed in different practical hydrologic applications, such as the construction of the intensity-duration-frequency curves (IDF). This paper presents an investigation of trends (station-wise and regional) for several rainfall durations that were constructed based on the 5 min rainfall data. Moreover, changes in IDF results for two 22-year sub-samples were also analyzed. Additionally, changes in extreme events clustering at the regional scale were also analyzed. Ten rainfall stations (44 years of data 1975−2018) located in Slovenia (central EU, approx. 20,000 km2) were used in this study. Results indicate that no clear pattern in the detected trends can be found based on the analyzed stations. However, all the statistically significant trends at the significance level of 0.05 for the 5 min rainfall data were negative. Moreover, regional trends for this duration were also statistically significant. The changes in the design rainfall events between two equal sub-samples (1975−1996, 1997−2018) were between −30% and 60%. The investigation of changes in extreme rainfall event clustering indicated that extreme 5, 30, and 60 min events could more frequently occur a few days earlier in spring or summer compared to the past period. On the other hand, longer duration events (i.e., 360 and 720 min) tend to more frequently occur a few days later in autumn compared to the past. In most cases, changes are not statistically significant.

It is not clear how projected climate change will impact the hydrological functioning of complex catchments that have significant karst characteristics. Therefore, in this paper we focused on the investigation of the low- and high-flow characteristics of the karst Ljubljanica River catchment. One smaller (51 km2) and one larger (1135 km2) catchment were selected in order to investigate the projected climate change impact on the hydrological conditions. For the investigation of the hydrological situation in the future, we used a lumped conceptual hydrological model. The model was calibrated using past measured daily data. Using the calibrated model, we investigated the impact of five different climate models outputs for the moderately optimistic scenario (RCP4.5). We investigated the situation in next 30-years periods: 2011−2040, 2041−2070, and 2071−2100. Several low and high-flow indices were calculated and compared. The results indicate that a summer precipitation decrease (i.e., 2011−2070) could lead to lower low-flow values for the investigated areas, which could increase the vulnerability of karst areas. Thus, additional focus should be given to water resource management in karst areas. On the other hand, mean flow could increase in the future. The same also applies for the high-flows where flood frequency analysis results indicate that a climate adaptation factor could be used for the hydrotechnical engineering design. However, differences among investigated models are large and show large variability among investigated cases.

Debris flows with different magnitudes can have a large impact on debris fan characteristics such as height or slope. Moreover, knowledge about the impact of random sequences of debris flows of different magnitudes on debris fan properties is sparse in the literature and can be improved using numerical simulations of debris fan formation. Therefore, in this paper we present the results of numerical simulations wherein we investigated the impact of a random sequence of debris flows on torrential fan formation, where the total volume of transported debris was kept constant, but different rheological properties were used. Overall, 62 debris flow events with different magnitudes from 100 m3 to 20,000 m3 were selected, and the total volume was approximately 225,000 m3. The sequence of these debris flows was randomly generated, and selected debris fan characteristics after the 62 events were compared. For modeling purposes, we applied the Rapid Mass Movement Simulations (RAMMS) software and its debris flow module (RAMMS-DF). The modeling was carried out using (a) real fan topography from an alpine environment (i.e., an actual debris fan in north-west (NW) Slovenia formed by the Suhelj torrent) and (b) an artificial surface with a constant slope. Several RAMMS model parameters were tested. The simulation results confirm that the random sequence of debris flow events has only some minor effects on the fan formation (e.g., slope, maximum height), even when changing debris flow rheological properties in a wide range. After the 62 events, independent of the selected sequence of debris flows, the final fan characteristics were not significantly different from each other. Mann⁻Whitney (MW) tests and t-tests were used for this purpose, and the selected significance level was 0.05. Moreover, this conclusion applies for artificial and real terrain and for a wide range of tested RAMMS model rheological parameters. Further testing of the RAMMS-DF model in real situations is proposed in order to better understand its applicability and limitations under real conditions for debris flow hazard assessment or the planning of mitigation measures.

Rainfall interception is an important process of the water cycle that can have significant influence on surface runoff and groundwater storage. Since rainfall interception measurements are rare and time consuming, rainfall interception estimation can be made indirectly using different meteorological variables. Experimental data of rainfall interception for birch and pine trees was measured at an experimental plot located in an urban area of Ljubljana, Slovenia in this study. A copula model was applied to predict the rainfall interception using meteorological variables, namely air temperature and vapour pressure deficit data. The copula model performance was compared to some other models such as decision trees, multiple linear regressions, and exponential functions. Using random sampling, we found that the copula model where Khoudraji-Liebscher copula functions were used yielded slightly smaller root mean square error (RMSE) and mean absolute error (MAE) values than other tested methods (i.e., RMSE and MAE results for birch trees were 24.2% and 18.2%, respectively and RMSE and MAE results for pine trees were 25.0% and 19.6%, respectively). The results demonstrate that the copula-based proposed method and other tested models could be used for the prediction of rainfall interception at the considered plot and in the wider surroundings. Furthermore, these models could also be applied for the prediction of rainfall interception for these two tree species in other locations under similar vegetation and meteorological conditions.

In the case of ungauged catchments, different procedures can be used to derive the design hydrograph and design peak discharge, which are crucial input data for the design of different hydrotechnical engineering structures, or the production of flood hazard maps. One of the possible approaches involves using a hydrological model where one can calculate the design hydrograph through the design of a rainfall event. This study investigates the impact of the design rainfall on the combined one-dimensional/two-dimensional (1D/2D) hydraulic modelling results. The Glinščica Stream catchment located in Slovenia (central Europe) is used as a case study. Ten different design rainfall events were compared for 10 and 100-year return periods, where we used Huff curves for the design rainfall event definition. The results indicate that the selection of the design rainfall event should be regarded as an important step, since the hydraulic modelling results for different scenarios differ significantly. In the presented experimental case study, the maximum flooded area extent was twice as large as the minimum one, and the maximum water velocity over flooded areas was more than 10 times larger than the minimum one. This can lead to the production of very different flood hazard maps, and consequently planning very different flood protection schemes.

Suspended sediment load (SSL) observations are usually less frequent than precipitation and river discharge measurements; therefore a reliable procedure is needed for the estimation of SSL. One year of precipitation, SSL, and discharge measurements at 20-min intervals were performed at the Kuzlovec torrent in Slovenia. The Frank copula was selected to construct an event-based model using the following variables: precipitation sum (P), peak discharge (Q), and SSL. The idea was to estimate the SSL based on the measured P and Q. The proposed model was additionally tested using the daily data from the Gornja Radgona station on the Mura River, for which 29 years of data were available and where Khoudraji-Liebscher copulas were used. The estimated SSL values using the copula were compared with different regression models. The proposed copula model yielded meaningful SSL estimates. Some performance criteria and tests indicated that the copula model gives a better fit to the measured data than other tested methods.