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The Mountain snowpack stores months of winter precipitation at high elevations, supplying snowmelt to lowland areas in drier seasons for agriculture and human consumption worldwide. Accurate seasonal predictions of the snowpack are thus of great importance, but such forecasts suffer from major challenges such as resolving interactions between forcing variables at high spatial resolutions. To test novel approaches to resolve these processes, seasonal snowpack simulations are run at different grid resolutions (50 m, 100 m, 250 m) and with variable forcing data for the water year 2016/2017. COSMO-1E data is either dynamically downscaled with the High-resolution Intermediate Complexity Atmospheric Research (HICAR) model or statistically downscaled to provide forcing data for snowpack simulations with the Flexible Snowpack Model (FSM2oshd). Simulations covering complex terrain in the Swiss Alps are carried out with the operational settings of the FSM2oshd model or with a model extension including windand gravitational-induced snow transport (FSM2trans). The simulated snow height is evaluated against observed snow height collected during LiDAR flights in spring 2017. Observed spatial snow accumulation patterns and snow height distribution are best matched with simulations using dynamically downscaled data and the FSM2trans model extension, indicating the importance of both accurate meteorological forcing data and snow transport schemes. This study demonstrates for the first time the effects of applying dynamical downscaling schemes to snowpack simulations at the seasonal and catchment scale. [ABSTRACT FROM AUTHOR]

Exploring the contributions of new and old water to runoff during precipitation events in agricultural catchments is essential for understanding runoff generation, solute transport, and soil erosion. The aim of this study was to investigate the variability in the isotopic composition of precipitation and runoff in the 66 ha agricultural catchment in Austria, in the Hydrological Open Air Laboratory (HOAL), in order to compare two isotope hydrograph separation methods. The classical two‐component (IHS) and the ensemble hydrograph separation (EHS) were applied to multiple large events in May–October of 2013–2018 using δ18O and δ2H. The peak flow new water contributions obtained by IHS were compared with the average new water fraction from EHS. The average new water fraction calculated with EHS based on regular weekly sampling was close to zero, which can be explained by the large diffuse groundwater discharge into the stream between the events. When only investigating events with high temporal resolution sampling, the results suggest that EHS provided average new water fractions during peak flows (0.46 ± 0.04 for δ18O, 0.47 ± 0.03 for δ2H) that were close to the averages obtained by IHS (0.47 for δ18O, 0.50 for δ2H). New water fractions tended to be higher for larger rainfall intensities. High peak flow new water fractions could be explained by the agricultural land use and soils with low permeability promoting overland flow generation and by some of the tile drainage systems contributing to the delivery of water. In conclusion, a weekly sampling frequency was not sufficient in the HOAL but instead high‐resolution sampling during events was necessary to estimate the average new water contributions during events. While EHS may be a more robust approach compared to IHS, as it relaxes some of the assumptions of IHS, IHS can provide information on the variability of new water contributions of individual events. [ABSTRACT FROM AUTHOR]

Flood hazard mapping is currently in a transitional phase involving the use of data and methods that were traditionally in the domain of local studies in a regional or nationwide context. Challenges include the representation of local information such as hydrological particularities and small hydraulic structures, as well as computational and labour costs. This paper proposes a methodology of flood hazard mapping that merges the best of the two worlds (local and regional studies) based on experiences in Austria. The analysis steps include (a) quality control and correction of river network and catchment boundary data; (b) estimation of flood discharge peaks and volumes on the entire river network; (c) creation of a digital elevation model (DEM) that is consistent with all relevant flood information, including riverbed geometry; and (d) simulation of inundation patterns and velocities associated with a consistent flood return period across the entire river network. In each step, automatic methods are combined with manual interventions in order to maximise the efficiency and at the same time ensure estimation accuracy similar to that of local studies. The accuracy of the estimates is evaluated in each step. The study uses flood discharge records from 781 stations to estimate flood hazard patterns of a given return period at a resolution of 2 m over a total stream length of 38 000 km. It is argued that a combined local–regional methodology will advance flood mapping, making it even more useful in nationwide or global contexts. [ABSTRACT FROM AUTHOR]

Flood hazard mapping is currently in a transitional phase involving the use of data and methods that were traditionally the domain of local studies in a regional or nation-wide context. Challenges include the representation of local information such as hydrological particularities and small hydraulic structures, and computational and labour costs. This paper proposes a methodology of flood hazard mapping that merges the best of the two worlds (local and regional studies) based on experiences in Austria. The analysis steps include (a) quality control and correction of river network and catchment boundary data; (b) estimation of flood discharge peaks and volumes on the entire river network; (c) creation of a digital elevation model (DEM) that is consistent with all relevant flood information, including river bed geometry; (d) simulation of inundation patterns and velocities associated with a consistent flood return period along the entire river network. In each step, automatic methods are combined with manual interventions in order to maximise the efficiency and at the same time ensure estimation accuracy similar to that of local studies. The accuracy of the estimates is evaluated in each step. The study uses flood discharge records from 781 stations to estimate flood hazard patterns of a given return period at a resolution of 2 m over a total stream length of 38000 km. It is argued that a combined local-regional methodology will advance flood mapping, making it even more useful in nation-wide or global contexts. [ABSTRACT FROM AUTHOR]

Spatially distributed hydrological model Mike SHE was used as a diagnostic tool to provide information on possible overland flow source areas in the mountain catchment of Jalovecký Creek (area 22.2 km2, elevation range 820–2178 m a.s.l.) during different rainfall-runoff events. Selected events represented a sequence of several smaller, consecutive events, a flash flood event and two large events caused by frontal precipitation. Simulation of hourly runoff was better for runoff events caused by heavy rainfalls of longer duration than for the flash flood or consecutive smaller runoff events. Higher soil moisture was simulated near the streamflow network and larger possibly saturated areas were located mainly in the upper parts of mountain valleys. The most pronounced increase in the areal extent of possibly saturated areas (from 6.5% to 68.6% of the catchment area) was simulated for the event with high peak discharge divided by a short rainfall interruption. Rainfall depth exceeding 100 mm caused a large increase in the potentially saturated areas that covered subsequently half of the catchment area or more. A maximum integral connectivity scale representing the average distance over which individual pixels were connected varied for the selected events between 45 and 6327 m. [ABSTRACT FROM AUTHOR]

In a previous study, the topsoil and root zone ASCAT satellite soil moisture data were implemented into three multi-objective calibration approaches of the TUW hydrological model in 209 Austrian catchments. This paper examines the model parametrization in those catchments, which in the validation of the dual-layer conceptual semi-distributed model showed improvement in the runoff simulation efficiency compared to the single objective runoff calibration. The runoff simulation efficiency of the three multi-objective approaches was separately considered. Inferences about the specific location and the physiographic properties of the catchments where the inclusion of ASCAT data proved beneficial were made. Improvements were primarily observed in the watersheds with lower slopes (median of the catchment slope less than 15 per cent) and a higher proportion of farming land use (median of the proportion of agricultural land above 20 per cent), as well as in catchments where the runoff is not significantly influenced by snowmelt and glacier runoff. Changes in the mean and variability of the field capacity parameter FC of the soil moisture regime were analysed. The values of FC decreased by 20 per cent on average. Consequently, the catchments' water balance closure generally improved by the increase in catchment evapotranspiration during the validation period. Improvements in model efficiency could be attributed to better runoff simulation in the spring and autumn month. The findings refine recommendations regarding when hydrological modelling could consider satellite soil moisture data added to runoff signatures in calibration useful. [ABSTRACT FROM AUTHOR]

Understanding the role of soil moisture and other controls in runoff generation is important for predicting runoff across scales. This paper aims to identify the degree of non‐linearity of the relationship between event peak runoff and potential controls for different runoff generation mechanisms in a small agricultural catchment. The study is set in the 66 ha Hydrological Open Air Laboratory, Austria, where discharge was measured at the catchment outlet and for 11 sub‐catchments or hillslopes with different runoff generation mechanisms. Peak runoff of 73 events was related to three potential controls: event precipitation, soil moisture and groundwater levels. The results suggest that the hillslopes dominated by ephemeral overland flow exhibit the most non‐linear runoff generation behaviour for its controls; runoff is only generated above a threshold of 95% of the maximum soil moisture. Runoff generation through tile drains and in wetlands is more linear. The largest winter and spring events at the catchment outlet are caused by runoff from hillslopes with shallow flow paths (ephemeral overland flow and tile drainage mechanisms), while the largest summer events are caused by other hillslopes, those with deeper flow paths or with saturation areas throughout the year. Therefore, the response of the entire catchment is a mix of the various mechanisms, and the groundwater contribution makes the response more linear. The implications for hydrological modelling are discussed. [ABSTRACT FROM AUTHOR]

The recent advances in remote sensing provide opportunities for estimating the parameters of conceptual hydrologic models more reliably. However, the question of whether and to what extent the use of satellite data in model calibration may assist in transferring model parameters to ungauged catchments has not been fully resolved. The aim of this study is to evaluate the efficiency of different methods for transferring model parameters obtained by multiple-objective calibrations to ungauged sites and to assess the model performance in terms of runoff, soil moisture, and snow cover predictions relative to existing regionalization approaches. The model parameters are calibrated to daily runoff, satellite soil moisture (Advanced Scatterometer – ASCAT), and snow cover (Moderate Resolution Imaging Spectroradiometer – MODIS) data. The assessment is based on 213 catchments situated in different physiographic and climate zones of Austria. For the transfer of model parameters, eight methods (global and local variants of arithmetic mean, regression, spatial proximity, and similarity) are examined in two periods, i.e., the period in which the model is calibrated (2000–2010) and an independent validation period (2010–2014). The predictive accuracy is evaluated by the leave-one-out cross-validation. The results show that the method by which the model is calibrated in the gauged catchment has a larger impact on runoff prediction accuracy in the ungauged catchments than the choice of the parameter transfer method. The best transfer methods are global and local similarity and the kriging approach. The performance of the transfer methods differs between lowland and alpine catchments. While the soil moisture and snow cover prediction efficiencies are higher in lowland catchments, the runoff prediction efficiency is higher in alpine catchments. A comparison of the model transfer methods, based on parameters calibrated to runoff, snow cover, and soil moisture with those based on parameters calibrated to runoff, only indicates that the former outperforms the latter in terms of simulating soil moisture and snow cover. The performance of simulating runoff is similar, and the accuracy depends mainly on the weight given to the runoff objective in the multiple-objective calibrations. [ABSTRACT FROM AUTHOR]

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Many recent studies have sought to characterize variations of the annual maximum flood discharge series over time and across space in Europe, including some that have elucidated different process controls on different statistical properties of these series. To further support these studies, we conduct a pan-European assessment of process controls on key properties of this series, including the mean annual flood (MAF) and coefficients of variation (CV) and skewness (CS) of flood discharges. These annual maximum flood discharge series consist of instantaneous peaks and daily means observed in 2370 catchments in Europe without strong human modifications covering the period 1960–2010. We explore how the estimated moments MAF, CV and CS vary due to catchment size, climate and other controls across Europe, where their averages are 0.17 m 3 s -1 km -2 , 0.52 and 1.28, respectively. The results indicate that MAF is largest along the Atlantic coast, in the high-rainfall areas of the Mediterranean coast and in mountainous regions, while it is smallest in the sheltered parts of the East European Plain. The CV is largest in southern and eastern Europe, while it is smallest in the regions subject to strong Atlantic influence. The pattern of the CS is similar, albeit more erratic, in line with the greater sampling variability of CS. In the Mediterranean, MAF, CV and CS decrease strongly with catchment area, suggesting that floods in small catchments are relatively very large, while in eastern Europe this dependence is much weaker, mainly due to more synchronized timing of snowmelt over large areas. The process controls on the flood moments in five predetermined hydroclimatic regions are identified through correlation and multiple linear regression analyses with a range of covariates, and the interpretation is aided by a seasonality analysis. Precipitation-related covariates are found to be the main controls of the spatial patterns of MAF in most of Europe except for regions in which snowmelt contributes to MAF, where air temperature is more important. The Aridity Index is, by far, the most important control on the spatial pattern of CV in all of Europe. Overall, the findings suggest that, at the continental scale, climate variables dominate over land surface characteristics, such as land use and soil type, in controlling the spatial patterns of flood moments. Finally, to provide a performance baseline for more local studies, we assess the estimation accuracy of regional multiple linear regression models for estimating flood moments in ungauged basins. [ABSTRACT FROM AUTHOR]

Following the rise of R as a scientific programming language, the increasing requirement for more transferable research and the growth of data availability in hydrology, R packages containing hydrological models are becoming more and more available as an open-source resource to hydrologists. Corresponding to the core of the hydrological studies workflow, their value is increasingly meaningful regarding the reliability of methods and results. Despite package and model distinctiveness, no study has ever provided a comparison of R packages for conceptual rainfall–runoff modelling from a user perspective by contrasting their philosophy, model characteristics and ease of use. We have selected eight packages based on our ability to consistently run their models on simple hydrology modelling examples. We have uniformly analysed the exact structure of seven of the hydrological models integrated into these R packages in terms of conceptual storages and fluxes, spatial discretisation, data requirements and output provided. The analysis showed that very different modelling choices are associated with these packages, which emphasises various hydrological concepts. These specificities are not always sufficiently well explained by the package documentation. Therefore a synthesis of the package functionalities was performed from a user perspective. This synthesis helps to inform the selection of which packages could/should be used depending on the problem at hand. In this regard, the technical features, documentation, R implementations and computational times were investigated. Moreover, by providing a framework for package comparison, this study is a step forward towards supporting more transferable and reusable methods and results for hydrological modelling in R. [ABSTRACT FROM AUTHOR]

Models that mimic an original model might have a different model structure than the original model, that affects model output. This study assesses model structure differences and their impact on output by comparing 7 model implementations that carry the name HBV. We explain and quantify output differences with individual model structure components at both the numerical (e.g., explicit/implicit scheme) and mathematical level (e.g., lineair/power outflow). It was found that none of the numerical and mathematical formulations of the mimicking models were (originally) the same as the benchmark, HBV‐light. This led to small but distinct output differences in simulated streamflow for different numerical implementations (KGE difference up to 0.15), and major output differences due to mathematical differences (KGE median loss of 0.27). These differences decreased after calibrating the individual models to the simulated streamflow of the benchmark model. We argue that the lack of systematic model naming has led to a diverging concept of the HBV‐model, diminishing the concept of model mimicry. Development of a systematic model naming framework, open accessible model code and more elaborate model descriptions are suggested to enhance model mimicry and model development. Key Points: The concept of model mimicry is evaluated by comparing models that bear the same nameA step‐wise comparison of model components against the benchmark model revealed a simulated outflow difference of up to 0.15 point in the Kling‐Gupta efficiency metric for numerical differencesModel structures are often difficult to access and need a systematic model naming framework to advance model development [ABSTRACT FROM AUTHOR]

The recent advances in remote sensing provide opportunities for more reliably estimating the parameters of conceptual hydrologic models. However, the question of whether and to what extent the use of satellite data in model calibration may assist in transferring model parameters to ungauged catchments has not been fully resolved. The aim of this study is to evaluate the efficiency of different methods for transferring model parameters obtained by multiple objective calibrations to ungauged sites and to assess the model performance in terms of runoff, soil moisture, and snow cover predictions relative to existing regionalization approaches. The model parameters are calibrated to daily runoff, satellite soil moisture (ASCAT), and snow cover (MODIS) data. The assessment is based on 213 catchments situated in different physiographic and climate zones of Austria. For the transfer of model parameters, eight methods (global and local variants of arithmetic mean, regression, spatial proximity, and similarity) are examined in two periods, i.e., the period in which the model is calibrated (2000-2010) and an independent validation period (2010-2014). The predictive accuracy is evaluated by leave-one-out cross-validation. The results show that the method by which the model is calibrated in the gauged catchment has a larger impact on runoff prediction accuracy in the ungauged catchments than the choice of the parameter transfer method. The best transfer methods are global and local similarity and the kriging approach. The performance of the transfer methods differs between lowland and alpine catchments. While the soil moisture and snow cover prediction efficiencies are higher in lowland catchments, the runoff prediction efficiency is higher in alpine catchments. A comparison of model transfer methods based on parameters calibrated to runoff, snow cover, and soil moisture with those based on parameters calibrated to runoff only indicates that the former outperforms the latter in terms of simulating soil moisture and snow cover. The performance of simulating runoff is similar, and the accuracy depends mainly on the weight given to the runoff objective in the multiple objective calibrations. [ABSTRACT FROM AUTHOR]

The Mediterranean region is a climate change hotspot for water resources. However, uncertainty analyses of hydrological projections are rarely quantified. In this study, an in-depth analysis of projections and uncertainties for high and low flows is performed. Climatic projections derived from a recent downscaling method were used, for two representative concentration pathway scenarios (RCPs), five general circulation model/regional climate model (GCM/RCM) couples, three hydrological models (HMs), and 29 calibration schemes. A quasi-ergodic analysis of variance was used to evaluate the contribution of each impact modelling step to the total uncertainty. For high flows, the results show a mean increase of 30% by 2085, and RCPs make the highest contribution to the total uncertainty, followed by GCMs. For low flows, 50% of projections indicate a decrease of 7% or more by 2085, and HM structures, hydrological model parameters, and GCMs are the most important uncertainty sources. These results contribute to raise awareness among water managers regarding future hydrological extreme events. [ABSTRACT FROM AUTHOR]

In this study, the value of proxy data was explored for calibrating a conceptual hydrologic model for small ungauged basins, i.e. ungauged in terms of runoff. The study site was a 66 ha Austrian experimental catchment dominated by agricultural land use, the Hydrological Open Air Laboratory (HOAL). The three modules of a conceptual, lumped hydrologic model (snow, soil moisture accounting and runoff generation) were calibrated step-by-step using only proxy data, and no runoff observations. Using this stepwise approach, the relative runoff volume errors in the calibration and first and second validation periods were –0.04, 0.19 and 0.17, and the monthly Pearson correlation coefficients were 0.88, 0.71 and 0.64, respectively. By using proxy data, the simulation of state variables improved compared to model calibration in one step using only runoff data. Using snow and soil moisture information for model calibration, the runoff model performance was comparable to the scenario when the model was calibrated using only runoff data. While the runoff simulation performance using only proxy data did not considerably improve compared to a scenario when the model was calibrated on runoff data, the more accurately simulated state variables imply that the process consistency improved. [ABSTRACT FROM AUTHOR]

Recent advances in soil moisture remote sensing have produced satellite data sets with improved soil moisture mapping under vegetation and with higher spatial and temporal resolutions. In this study, we evaluate the potential of a new, experimental version of the Advanced Scatterometer (ASCAT) soil water index data set for multiple objective calibrations of a conceptual hydrologic model. The analysis is performed in 213 catchments in Austria for the period 2000–2014. An HBV (Hydrologiska Byråns Vattenbalansavdelning)-type hydrologic model is calibrated based on runoff data, ASCAT soil moisture data, and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data for various calibration variants. Results show that the inclusion of soil moisture data in the calibration mainly improves the soil moisture simulations, the inclusion of snow data mainly improves the snow simulations, and the inclusion of both of them improves both soil moisture and snow simulations to almost the same extent. The snow data are more efficient at improving snow simulations than the soil moisture data are at improving soil moisture simulations. The improvements of both runoff and soil moisture model efficiencies are larger in low elevation and agricultural catchments than in others. The calibrated snow-related parameters are strongly affected by including snow data and, to a lesser extent, by soil moisture data. In contrast, the soil-related parameters are only affected by the inclusion of soil moisture data. The results indicate that the use of multiple remote sensing products in hydrological modeling can improve the representation of hydrological fluxes and prediction of runoff hydrographs at the catchment scale. [ABSTRACT FROM AUTHOR]

This paper analyses the spatial patterns and process controls of the mean annual flood (MAF), coefficient of variation (CV) and skewness (CS) of flood discharges in Europe. The data consist of maximum annual flood discharge series observed in 2370 catchments in Europe covering the period 1960-2010. On average, in Europe, the estimated moments MAF, CV and CS are 0.17 m3 s−1 km−2, 0.52 and 1.28, respectively. The results indicate that MAF is largest along the Atlantic coast, the high rainfall areas of the Mediterranean coast and in the mountain ranges of Europe, while it is smallest in the sheltered parts of the East European plain. CV is largest in Southern and Eastern Europe, while it is smallest in the regions of strong Atlantic influence. The pattern of CS is similar to that of CV, albeit a little more erratic. In the Mediterranean, MAF, CV and CS decrease strongly with catchment area suggesting that floods in small catchments are relatively very large, while in Eastern Europe the dependence is much weaker. The Process controls on the flood moments in five hydroclimatic regions are identified by correlation and multiple linear regression analyses with a range of covariates. Precipitation-related variables are found to be the main controls of the spatial patterns of MAF in most of Europe except for the regions with snow influence where air temperature is more important. Catchment area is another relevant variable. Aridity is by far the most important control of the spatial pattern of CV in all of Europe. Precipitation-related variables are relevant in Southern Europe. Overall, the findings suggest that, at the continental scale, climate variables dominate over land surface characteristics, such as land use and soil type, in controlling the spatial patterns of flood moments. Finally, the estimation accuracy of the multiple linear regression model for estimating flood moments in ungauged basins is evaluated as a baseline for more accurate local studies. [ABSTRACT FROM AUTHOR]

The setup of a rainfall-runoff model in a river section where no streamflow measurements are available for its calibration is one of the key research activities for the Prediction in Ungauged Basins (PUB): in order to do so it is possible to estimate the model parameters based on the hydrometric information available in the region. The informative content of the dataset (i.e. which and how many gauged river stations are available) plays an essential role in the assessment of the best regionalisation method. This study analyses how the performances of regionalisation approaches are influenced by the "information richness" of the available regional dataset, i.e. the availability of potential donors, and in particular by the gauging density and by the presence of nested donor catchments, which are expected to be hydrologically very similar to the target section. The research is carried out over a densely gauged dataset covering the Austrian country, applying two rainfall-runoff models and different regionalisation approaches. The regionalisation techniques are first implemented using all the gauged basins in the dataset as potential donors and then re-applied, decreasing the informative content of the dataset. The effect of excluding nested basins and the status of "nestedness" is identified based on the position of the closing section along the river or the percentage of shared drainage area. Moreover, the impact of reducing station density on regionalisation performance is analysed. The results show that the predictive accuracy of parameter regionalisation techniques strongly depends on the informative content of the dataset of available donor catchments. The "output-averaging" approaches, which exploit the information of more than one donor basin and preserve the correlation structure of the parameter, seem to be preferable for regionalisation purposes in both data-poor and data-rich regions. Moreover, with the use of an optimised set of catchment descriptors as a similarity measure, rather than the simple geographical distance, results are more robust to the deterioration of the informative content of the set of donors. [ABSTRACT FROM AUTHOR]

Following the rise of R as a scientific programming language, the increasing requirement for more transferable research, and the growth of data availability in hydrology, R packages containing hydrological models are becoming more and more available to hydrologists. Corresponding to the core of the hydrological studies workflow, their value is increasingly meaningful regarding the reliability of methods and results. Despite package and model distinctiveness, no study has ever provided a comparison of R packages for conceptual rainfall-runoff modelling from a user perspective, contrasting their philosophy, model characteristics and ease of use. We have selected eight packages based on our ability to consistently run their models on simple hydrology modelling examples. We have uniformly analysed the exact structure of seven of the hydrological models integrated in these R packages in terms of conceptual storages and fluxes, spatial discretisation, data requirements and output provided. The analysis showed that very different modelling choices are associated with these packages, which emphasises various hydrological concepts. These specificities are not always sufficiently well explained by the package documentation. Therefore a synthesis of the package functionalities was performed from a user perspective. This synthesis helps inform selection of what packages could/should be used depending on the problem at hand. In this regard, technical features, documentation, R implementations and computational times were investigated. Moreover, by providing a framework for package comparison, this study is a step forward towards supporting more transferable and reusable methods and results for hydrological modelling in R. [ABSTRACT FROM AUTHOR]

The objective of this study is to investigate the factors that control event runoff characteristics at the small catchment scale. The study area is the Hydrological Open Air Laboratory, Lower Austria. Event runoff coefficient (Rc), recession time constant (Tc) and peak discharge (Qp) are estimated from hourly discharge and precipitation data for 298 events in the period 2013–2015. The results show that the Rc and their variability tend to be largest for the tile drainages (mean Rc = 0.09) and the main outlet (mean Rc = 0.08) showing larger Rc in January/February and smaller Rc in July/August. Tc does not vary much between the systems and tends to be largest at the main outlet (mean Tc = 6.5 h) and smallest for the tile drainages (mean Tc = 4.5 h). Groundwater levels explain the temporal variability of Rc and Tc more than soil moisture or precipitation, suggesting a role of shallow flow paths. [ABSTRACT FROM AUTHOR]

Recent advances in soil moisture remote sensing have produced satellite datasets with improved soil moisture mapping under vegetation and with higher spatial and temporal resolutions. In this study, we evaluate the potential of a new, experimental version of the ASCAT Soil Water Index dataset for multiple objective calibration of a conceptual hydrologic model. The analysis is performed in 213 catchments in Austria for the period 2000-2014. An HBV type hydrologic model is calibrated to runoff data, ASCAT soil moisture data, and MODIS snow cover data for various calibration variants. Results show that the inclusion of soil moisture data in the calibration mainly improves the soil moisture simulations; the inclusion of snow data mainly improves the snow simulations; and including both of them improves both soil moisture and snow simulations to almost the same extent. The snow data are more efficient in improving snow simulations than the soil moisture data are in improving soil moisture simulations. The improvements of both runoff and soil moisture model efficiencies are larger in low elevation and agricultural catchments than in others. The calibrated snow-related parameters are strongly affected by including snow data, and to a lesser extent by soil moisture data, while the soil-related parameters are only affected by the inclusion of soil moisture data. [ABSTRACT FROM AUTHOR]

A rainfall–runoff model was employed to identify four major flood-generating processes corresponding to flood events identified from daily discharge data from 614 stations across Europe in the period 1961–2010: long-rain, short-rain, snowmelt, and rain-on-dry-soil flood events. Trend analyses were performed on the frequency of occurrence of each of the flood types continentally and in five geographical regions of Europe. Continentally, the annual frequency of flood events did not show a significant change over the investigation period. However, the frequency of both winter and summer long-rain events increased significantly, while that of summer snowmelt events decreased significantly. Regionally, the frequency of winter short and long-rain events increased significantly in Western Europe, while the frequency of summer snowmelt and short-rain events decreased in Northern Europe. The frequency of summer snowmelt events in Eastern Europe and winter short-rain events in Southern Europe showed a declining trend. [ABSTRACT FROM AUTHOR]

Several studies have shown that hydrological models do not perform well when applied to periods with climate conditions that differ from those during model calibration. This has important implications for the application of these models in climate change impact studies. The causes of the low transferability to changed climate conditions have, however, only been investigated in a few studies. Here we revisit a study in Austria that demonstrated the inability of a conceptual semi-distributed HBV-type model to simulate the observed discharge response to increases in precipitation and air temperature. The aim of the paper is to shed light on the reasons for these model problems. We set up hypotheses for the possible causes of the mismatch between the observed and simulated changes in discharge and evaluate these using simulations with modifications of the model. In the baseline model, trends of simulated and observed discharge over 1978–2013 differ, on average over all 156 catchments, by 95±50 mm yr -1 per 35 years. Accounting for variations in vegetation dynamics, as derived from a satellite-based vegetation index, in the calculation of reference evaporation explains 36±9 mm yr -1 per 35 years of the differences between the trends in simulated and observed discharge. Inhomogeneities in the precipitation data, caused by a variable number of stations, explain 39±26 mm yr -1 per 35 years of this difference. Extending the calibration period from 5 to 25 years, including annually aggregated discharge data or snow cover data in the objective function, or estimating evaporation with the Penman–Monteith instead of the Blaney–Criddle approach has little influence on the simulated discharge trends (5 mm yr -1 per 35 years or less). The precipitation data problem highlights the importance of using precipitation data based on a stationary input station network when studying hydrologic changes. The model structure problem with respect to vegetation dynamics is likely relevant for a wide spectrum of regions in a transient climate and has important implications for climate change impact studies. [ABSTRACT FROM AUTHOR]

Editors of several journals in the field of hydrology met during the General Assembly of the European Geosciences Union—EGU in Vienna in April 2017. This event was a follow‐up of similar meetings held in 2013 and 2015. These meetings enable the group of editors to review the current status of the journals and the publication process and to share thoughts on future strategies. Journals were represented at the 2017 meeting by their editors, as shown in the list of authors. The main points on invigorating hydrological research through journal publications are communicated in this joint editorial published in the above journals. [ABSTRACT FROM AUTHOR]

The paper introduces the Special Section on the Hydrology of the Carpathians in this issue. It is the result of an initiative of the Department of Land and Water Resources Management of the Slovak University of Technology in Bratislava, the Institute of Hydraulic Engineering and Water Resources Management of the TU Vienna and the Institute of Geomatics and Civil Engineering of the University of Sopron to allow young hydrologists in the Carpathian Basin (and from outside) to present their research and re-network on the emerging topics of the hydrology of the Carpathians at the HydroCarpath Conferences since 2012. [ABSTRACT FROM AUTHOR]

Measuring evaporation and transpiration at the field scale is complicated due to the heterogeneity of the environment, with point measurements requiring upscaling and field measurements such as eddy covariance measuring only the evapotranspiration. During the summer of 2014 an eddy covariance device was used to measure the evapotranspiration of a growing maize field at the HOAL catchment. The stable isotope technique and a Lagrangian near field theory (LNF) were then utilized to partition the evapotranspiration into evaporation and transpiration, using the concentration and isotopic ratio of water vapour within the canopy. The stable isotope estimates of the daily averages of the fraction of evapotranspiration (Ft) ranged from 43.0–88.5%, with an average value of 67.5%, while with the LNF method, Ft was found to range from 52.3–91.5% with an average value of 73.5%. Two different parameterizations for the turbulent statistics were used, with both giving similar R2 values, 0.65 and 0.63 for the Raupach and Leuning parameterizations, with the Raupach version performing slightly better. The stable isotope method demonstrated itself to be a more robust method, returning larger amounts of useable data, however this is limited by the requirement of much more additional data. [ABSTRACT FROM AUTHOR]

The event runoff coefficient (Rc) and the recession coefficient (tc) are of theoretical importance for understanding catchment response and of practical importance in hydrological design. We analyse 57 event periods in the period 2013 to 2015 in the 66 ha Austrian Hydrological Open Air Laboratory (HOAL), where the seven subcatchments are stratified by runoff generation types into wetlands, tile drainage and natural drainage. Three machine learning algorithms (Random forest (RF), Gradient Boost Decision Tree (GBDT) and Support vector machine (SVM)) are used to estimate Rc and tc from 22 event based explanatory variables representing precipitation, soil moisture, groundwater level and season. The model performance of the SVM algorithm in estimating Rc and tc is generally higher than that of the other two methods, measured by the coefficient of determination R2, and the performance for Rc is higher than that for tc. The relative importance of the explanatory variables for the predictions, assessed by a heatmap, suggests that Rc of the tile drainage systems is more strongly controlled by the weather conditions than by the catchment state, while the opposite is true for natural drainage systems. Overall, model performance strongly depends on the runoff generation type. [ABSTRACT FROM AUTHOR]

The set up of a rainfall-runoff model in a river section where no streamflow measurements are available for its calibration is one of the key research activity for the Prediction in Ungauged Basins (PUB): in order to do so it is possible to regionalise the model parameters based on the information available in gauged sections in the study region. The information content in the data set of gauged river stations plays an essential role in the assessment of the best regionalisation method: this study analyses how the performances of different model regionalisation approaches are influenced by the information richness of the available regional data set, and in particular by its gauging density and by the presence of nested catchments, that are expected to be hydrologically very similar. The research is carried out over a densely gauged dataset covering the Austrian country, applying two different rainfall-runoff models: a semi-distributed version of the HBV model (TUW model), and the Cemaneige-GR6J model. The regionalisation approaches include both methods which transfer the entire set of model parameters from donor catchments, thus maintaining correlation among parameters (output averaging techniques), and methods which derive each target parameter independently, as a function of the calibrated donors' ones (parameter averaging techniques). The regionalisation techniques are first implemented using all the basins in the dataset as potential donors, showing that the output-averaging methods outperform the parameter-averaging kriging method, highlighting the importance of maintaining the correlation between the parameter values. The regionalisation is then repeated decreasing the information content of the data set, by excluding the nested basins, identified taking into account either the position of the closing section along the river or the percentage of shared drainage area. The parameter-averaging kriging is the method that is less impacted by the exclusion of the nested donors, whereas the methods transferring the entire parameter set from only one donor suffer the highest deterioration, since the single most similar or closest donor is often a nested one. On the other hand, the output-averaging methods degrade more gracefully, showing that exploiting the information resulting from more than one donor increases the robustness of the approach also in regions that do not have so many nested catchments as the Austrian one. Finally, the deterioration resulting from decreasing the station density on the regionalisation was analysed, showing that the output averaging methods using as similarity measure a set of catchment descriptors, rather than the geographical distance, are more capable to adapt to less dense datasets. The study confirms how the predictive accuracy of parameter regionalisation techniques strongly depends on the information content of the dataset of available donor catchments and indicates that the output-averaging approaches, using more than one donor basin but preserving the correlation structure of the parameter set, seem to be preferable for regionalisation purposes in both data-poor and data-rich regions. [ABSTRACT FROM AUTHOR]

Several studies have shown that hydrological models do not perform well when applied to periods with climate conditions that differ from those during model calibration. This has important implications for the application of these models in climate change impact studies. The causes of the low transferability to changed climate conditions have, however, only been investigated in a few studies. Here we revisit a study in Austria that demonstrated the inability of a conceptual model to simulate the discharge response to increases in precipitation and air temperature. The aim of the paper is to shed light on the reasons of these model problems. We set up hypotheses for the possible causes of the mismatch between the observed and simulated changes in discharge and evaluate these using simulations with modifications of the model. In the baseline model, trends of simulated and observed discharge over 1978-2013 differ, on average over all 156 catchments, by 92 ± 50 mm yr-1 per 35 yrs. Accounting for variations in vegetation dynamics, as derived from a satellite-based vegetation index, in the calculation of reference evaporation explains 35 ± 9 mm yr-1 per 35 yrs of the differences between the trends in simulated and observed discharge. Inhomogeneities in the precipitation data, caused by a variable number of stations explain 44 ± 28 mm yr-1 per 35 yrs of this difference. Extending the calibration period from 5 to 25 yrs, varying the objective function by including annually aggregated discharge data, or estimating evaporation with the Penman-Monteith instead of the Blaney-Criddle approach has little influence on the simulated discharge trends. The precipitation data problem highlights the importance of using precipitation data based on a stationary input station network when studying hydrologic changes. The model structure problem with respect to vegetation dynamics is likely relevant for a wide spectrum of regions in a transient climate and has important implications for climate change impact studies. [ABSTRACT FROM AUTHOR]

Climate change has led to concerns about increasing river floods resulting from the greater water-holding capacity of a warmer atmosphere1. These concerns are reinforced by evidence of increasing economic losses associated with flooding in many parts of the world, including Europe2. Any changes in river floods would have lasting implications for the design of flood protection measures and flood risk zoning. However, existing studies have been unable to identify a consistent continental-scale climatic-change signal in flood discharge observations in Europe3, because of the limited spatial coverage and number of hydrometric stations. Here we demonstrate clear regional patterns of both increases and decreases in observed river flood discharges in the past five decades in Europe, which are manifestations of a changing climate. Our results—arising from the most complete database of European flooding so far—suggest that: increasing autumn and winter rainfall has resulted in increasing floods in northwestern Europe; decreasing precipitation and increasing evaporation have led to decreasing floods in medium and large catchments in southern Europe; and decreasing snow cover and snowmelt, resulting from warmer temperatures, have led to decreasing floods in eastern Europe. Regional flood discharge trends in Europe range from an increase of about 11 per cent per decade to a decrease of 23 per cent. Notwithstanding the spatial and temporal heterogeneity of the observational record, the flood changes identified here are broadly consistent with climate model projections for the next century4,5, suggesting that climate-driven changes are already happening and supporting calls for the consideration of climate change in flood risk management. Analysis of a comprehensive European flood dataset reveals regional changes in river flood discharges in the past five decades that are consistent with models suggesting that climate-driven changes are already happening. [ABSTRACT FROM AUTHOR]

This study evaluates MODIS snow cover characteristics for large number of snowmelt runoff events in 145 catchments from 9 countries in Europe. The analysis is based on open discharge daily time series from the Global Runoff Data Center database and daily MODIS snow cover data. Runoff events are identified by a base flow separation approach. The MODIS snow cover characteristics are derived from Terra 500 m observations (MOD10A1 dataset, V005) in the period 2000-2015 and include snow cover area, cloud coverage, regional snowline elevation (RSLE) and its changes during the snowmelt runoff events. The snowmelt events are identified by using estimated RSLE changes during a runoff event. The results indicate that in the majority of catchments there are between 3 and 6 snowmelt runoff events per year. The mean duration between the start and peak of snowmelt runoff events is about 3 days and the proportion of snowmelt events in all runoff events tends to increase with the maximum elevation of catchments. Clouds limit the estimation of snow cover area and RSLE, particularly for dates of runoff peaks. In most of the catchments, the median of cloud coverage during runoff peaks is larger than 80%. The mean minimum RSLE, which represents the conditions at the beginning of snowmelt events, is situated approximately at the mean catchment elevation. It means that snowmelt events do not start only during maximum snow cover conditions, but also after this maximum. The mean RSLE during snowmelt peaks is on average 170 m lower than at the start of the snowmelt events, but there is a large regional variability. [ABSTRACT FROM AUTHOR]

The geophysical and hydrological processes governing river flow formation exhibit persistence at several timescales, which may manifest itself with the presence of positive seasonal correlation of streamflow at several different time lags. We investigate here how persistence propagates along subsequent seasons and affects low and high flows. We define the high-flow season (HFS) and the low-flow season (LFS) as the 3-month and the 1-month periods which usually exhibit the higher and lower river flows, respectively. A dataset of 224 rivers from six European countries spanning more than 50 years of daily flow data is exploited. We compute the lagged seasonal correlation between selected river flow signatures, in HFS and LFS, and the average river flow in the antecedent months. Signatures are peak and average river flow for HFS and LFS, respectively. We investigate the links between seasonal streamflow correlation and various physiographic catchment characteristics and hydro-climatic properties. We find persistence to be more intense for LFS signatures than HFS. To exploit the seasonal correlation in the frequency estimation of high and low flows, we fit a bi-variate meta-Gaussian probability distribution to the selected flow signatures and average flow in the antecedent months in order to condition the distribution of high and low flows in the HFS and LFS, respectively, upon river flow observations in the previous months. The benefit of the suggested methodology is demonstrated by updating the frequency distribution of high and low flows one season in advance in a real-world case. Our findings suggest that there is a traceable physical basis for river memory which, in turn, can be statistically assimilated into high- and low-flow frequency estimation to reduce uncertainty and improve predictions for technical purposes. [ABSTRACT FROM AUTHOR]

Editors of several journals in the field of hydrology met during the General Assembly of the European Geosciences Union - EGU in Vienna in April 2017. This event was a follow-up of similar meetings held in 2013 and 2015. These meetings enable the group of editors to review the current status of the journals and the publication process, and to share thoughts on future strategies. Journals were represented at the 2017 meeting by their editors, as shown in the list of authors. The main points on invigorating hydrological research through journal publications are communicated in this joint editorial published in the journals listed here. [ABSTRACT FROM AUTHOR]

In many Austrian catchments in recent decades an increase in the mean annual air temperature and precipitation has been observed, but only a small change in the mean annual runoff. The main objective of this paper is (1) to analyze alterations in the performance of a conceptual hydrological model when applied in changing climate conditions and (2) to assess the factors and model parameters that control these changes. A conceptual rainfall-runoff model (the TUW model) was calibrated and validated in 213 Austrian basins from 1981–2010. The changes in the runoff model's efficiency have been compared with changes in the mean annual precipitation and air temperature and stratified for basins with dominant snowmelt and soil moisture processes. The results indicate that while the model's efficiency in the calibration period has not changed over the decades, the values of the model's parameters and hence the model's performance (i.e., the volume error and the runoff model's efficiency) in the validation period have changed. The changes in the model's performance are greater in basins with a dominant soil moisture regime. For these basins, the average volume error which was not used in calibration has increased from 0% (in the calibration periods 1981–1990 or 2001–2010) to 9% (validation period 2001–2010) or –8% (validation period 1981–1990), respectively. In the snow-dominated basins, the model tends to slightly underestimate runoff volumes during its calibration (average volume error = –4%), but the changes in the validation periods are very small (i.e., the changes in the volume error are typically less than 1–2%). The model calibrated in a colder decade (e.g., 1981–1990) tends to overestimate the runoff in a warmer and wetter decade (e.g., 2001–2010), particularly in flatland basins. The opposite case (i.e., the use of parameters calibrated in a warmer decade for a colder, drier decade) indicates a tendency to underestimate runoff. A multidimensional analysis by regression trees showed that the change in the simulated runoff volume is clearly related to the change in precipitation, but the relationship is not linear in flatland basins. The main controlling factor of changes in simulated runoff volumes is the magnitude of the change in precipitation for both groups of basins. For basins with a dominant snowmelt runoff regime, the controlling factors are also the wetness of the basins and the mean annual precipitation. For basins with a soil moisture regime, landcover (forest) plays an important role. [ABSTRACT FROM AUTHOR]

Editors of several journals in the field of hydrology met during the General Assembly of the European Geosciences Union (EGU) in Vienna in April 2017. This event was a follow-up of similar meetings held in 2013 and 2015. These meetings enable the group of editors to review the current status of the journals and the publication process, and to share thoughts on future strategies. Journals were represented at the 2017 meeting by their editors, as shown in the list of authors. The main points on invigorating hydrological research through journal publications are communicated in this joint editorial published in the above journals. [ABSTRACT FROM AUTHOR]

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Abstract: Editors of several journals in the field of hydrology met during the General Assembly of the European Geosciences Union—EGU in Vienna in April 2017. This event was a follow‐up of similar meetings held in 2013 and 2015. These meetings enable the group of editors to review the current status of the journals and the publication process and to share thoughts on future strategies. Journals were represented at the 2017 meeting by their editors, as shown in the list of authors. The main points on invigorating hydrological research through journal publications are communicated in this joint editorial published in the above journals. [ABSTRACT FROM AUTHOR]

Since the beginning of hydrological research hydrologists have developed models that reflect their perception about how the catchments work and make use of the available information in the most efficient way. In this paper we develop hydrologic models based on field-mapped runoff generation mechanisms as identified by a geologist. For four different catchments in Austria, we identify four different lumped model structures and constrain their parameters based on the field-mapped information. In order to understand the usefulness of geologic information, we test their capability to predict river discharge in different cases: (i) without calibration and (ii) using the standard split-sample calibration/validation procedure. All models are compared against each other. Results show that, when no calibration is involved, using the right model structure for the catchment of interest is valuable. A-priori information on model parameters does not always improve the results but allows for more realistic model parameters. When all parameters are calibrated to the discharge data, the different model structures do not matter, i.e., the differences can largely be compensated by the choice of parameters. When parameters are constrained based on field-mapped runoff generation mechanisms, the results are not better but more consistent between different calibration periods. Models selected by runoff generation mechanisms are expected to be more robust and more suitable for extrapolation to conditions outside the calibration range than models that are purely based on parameter calibration to runoff data. [ABSTRACT FROM AUTHOR]

Editors of several journals in the field of hydrology met during the General Assembly of the European Geosciences Union-EGU in Vienna in April 2017. This event was a follow-up of similar meetings held in 2013 and 2015. These meetings enable the group of editors to review the current status of the journals and the publication process, and to share thoughts on future strategies. Journals were represented at the 2017 meeting by their editors, as shown in the list of authors. The main points on invigorating hydrological research through journal publications are communicated in this joint editorial published in the above journals. [ABSTRACT FROM AUTHOR]

Our study develops and tests a geostatistical technique for locally enhancing macro-scale rainfall-runoff simulations on the basis of observed streamflow data that were not used in calibration. We consider Tyrol (Austria and Italy) and two different types of daily streamflow data: macroscale rainfall-runoff simulations at 11 prediction nodes and observations at 46 gauged catchments. The technique consists of three main steps: (1) period-of-record flow-duration curves (FDCs) are geostatistically predicted at target ungauged basins, for which macro-scale model runs are available; (2) residuals between geostatistically predicted FDCs and FDCs constructed from simulated streamflow series are computed; (3) the relationship between duration and residuals is used for enhancing simulated time series at target basins. We apply the technique in cross-validation to 11 gauged catchments, for which simulated and observed streamflow series are available over the period 1980-2010. Our results show that (1) the procedure can significantly enhance macro-scale simulations (regional LNSE increases from nearly zero to ~ 0:7) and (2) improvements are significant for low gauging network densities (i.e. 1 gauge per 2000 km²). [ABSTRACT FROM AUTHOR]

Editors of several journals in the field of hydrology met during the General Assembly of the European Geosciences Union—EGU in Vienna in April 2017. This event was a follow-up of similar meetings held in 2013 and 2015. These meetings enable the group of editors to review the current status of the journals and the publication process, and to share thoughts on future strategies. Journals were represented at the 2017 meeting by their editors, as shown in the list of authors. The main points on invigorating hydrological research through journal publications are communicated in this joint editorial published in the above journals. [ABSTRACT FROM AUTHOR]

The geophysical and hydrological processes governing river flow formation exhibit persistence at several timescales, which may manifest itself with the presence of positive seasonal correlation of streamflow at several different time lags. We investigate here how persistence propagates along subsequent seasons and affects low and high flows. We define the High Flow Season (HFS) and the Low Flow Season (LFS) as the three-month and the one-month periods which usually exhibit the higher and lower river flows, respectively. A dataset of 224 European rivers spanning more than 50 years of daily flow data is exploited. We compute the lagged seasonal correlation between selected river flow signatures, in HFS and LFS, and the average river flow in the antecedent months. Signatures are peak and average river flow for HFS and LFS, respectively. We investigate the links between seasonal streamflow correlation and various physiographic catchment characteristics and hydro-climatic properties. We find persistence to be more intense for LFS signatures than HFS. To exploit the seasonal correlation in flood frequency estimation, we fit a bivariate Meta-Gaussian probability distribution to peak HFS flow and average pre-HFS flow in order to condition the peak flow distribution in the HFS upon river flow observations in the previous months. The benefit of the suggested methodology is demonstrated by updating the flood frequency distribution one season in advance in real-world cases. Our findings suggest that there is a traceable physical basis for river memory which in turn can be statistically assimilated into flood frequency estimation to reduce uncertainty and improve predictions for technical purposes. [ABSTRACT FROM AUTHOR]

This study analyses the differences in significant trends in magnitude and frequency of floods detected in annual maximum flood (AMF) and peak over threshold (POT) flood peak series, for the period 1965-2005. Flood peaks are identified from European daily discharge data using a baseflow-based algorithm and significant trends in the AMF series are compared with those in the POT series, derived for six different exceedence thresholds. The results show that more trends in flood magnitude are detected in the AMF than in the POT series and for the POT series more significant trends are detected in flood frequency than in flood magnitude. Spatially coherent patterns of significant trends are detected, which are further investigated by stratifying the results into five regions based on catchment and hydro-climatic characteristics. All data and tools used in this study are open-access and the results are fully reproducible. [ABSTRACT FROM AUTHOR]

Editors of several journals in the field of hydrology met during the General Assembly of the European Geosciences Union (EGU) in Vienna in April 2017. This event was a follow‐up of similar meetings held in 2013 and 2015. These meetings enable the group of editors to review the current status of the journals and the publication process, and to share thoughts on future strategies. Journals were represented at the 2017 meeting by their editors, as shown in the list of authors. The main points on invigorating hydrological research through journal publications are communicated in this joint editorial published in our journals. [ABSTRACT FROM AUTHOR]

The aim of this study is to understand the seasonalities of runoff and precipitation and their controls along two transects in Peru and one transect in Austria. The analysis is based on daily precipitation data at 111 and 61 stations in Peru and Austria, respectively, and daily discharge data at 51 and 110 stations. The maximum Pardé coefficient is used to quantify the strength of the seasonalities of monthly precipitation and runoff. Circular statistics are used to quantify the seasonalities of annual maximum daily precipitation and annual maximum daily runoff. The results suggest that much larger spatial variation in seasonality in Peru is because of the large diversity in climate and topography. In the dry Peruvian lowlands of the North, the strength of the monthly runoff seasonality is smaller than that of precipitation due to a relatively short rainy period from January to March, catchment storage and the effect of upstream runoff contributions that are more uniform within the year. In the Peruvian highlands in the South, the strength of the monthly runoff seasonality is greater than that of precipitation, or similar, due to relatively little annual precipitation and rather uniform evaporation within the year. In the Austrian transect, the strength of the runoff seasonality is greater than that of precipitation due to the influence of snowmelt in April to June. The strength of monthly regime of precipitation and runoff controls the concentration of floods and extreme precipitation in Peruvian transects. The regions with strong monthly seasonality of runoff have also extreme events concentrated along the same time of the year and the occurrence of floods is mainly controlled by the seasonality of precipitation. In Austria, the monthly runoff maxima and floods occur in the same season in the Alps. In the lowlands, the flood seasonality is controlled mainly by summer extreme precipitation and its interplay with larger soil moisture. The analyses of precipitation and runoff data along topographic gradients in Peru and Austria showed that, overall, in Peru the spatial variation in seasonality is much larger than in Austria. This is because of the larger diversity in climate and topography. [ABSTRACT FROM AUTHOR]