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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]

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]

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]

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]

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]

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]

Mapping saturation areas during rainfall events is important for understanding the dynamics of overland flow. In this study, we evaluate the potential of high temporal resolution time-lapse photography for mapping the dynamics of saturation areas (i.e., areas where water is visually ponding on the surface) on the hillslope scale during natural rainfall. We take 1 image per minute over a 100 × 15 m2 depression area on an agricultural field in the Hydrological Open Air Laboratory, Austria. The images are georectified and classified by an automated procedure, using grey intensity as a threshold to identify saturation area. The optimum threshold T is obtained by comparing saturation areas from the automated analysis with the manual analysis of 149 images. T is found to be highly correlated with an image brightness characteristic defined as the greyscale image histogram mode M (Pearson correlation r = 0.91). We estimate T as T = M + C where C is a calibration parameter assumed to be constant during each event. The automated procedure estimates the total saturation area close to the manual analysis with mean normalized root mean square error of 9% and 21% if C is calibrated for each event and taken constant for all events, respectively. The spatial patterns of saturation are estimated with a geometric mean accuracy index of 94% as compared to the manual analysis of the same photos. The patterns are tested against field observations for one date as a preliminary demonstration, which yields a root mean square error of the shortest distance between the measured boundary points and the automatically classified boundary as 23 cm. The usefulness of the patterns is illustrated by exploring run-off generation processes of an example event. Overall, the proposed classification method based on grey intensity is found to process images with highly varying brightnesses well. It is more efficient than the manual tracing for a large number of images, which allows the exploration of surface flow processes at high temporal resolution. [ABSTRACT FROM AUTHOR]

The case study aims at selecting optimal bivariate copula models of the relationships between flood peaks and flood volumes from a regional perspective with a particular focus on flood generation processes. Besides the traditional approach that deals with the annual maxima of flood events, the current analysis also includes all independent flood events. The target region is located in the northwest of Austria; it consists of 69 small and mid-sized catchments. On the basis of the hourly runoff data from the period 1976-2007, independent flood events were identified and assigned to one of the following three types of flood categories: synoptic floods, flash floods and snowmelt floods. Flood events in the given catchment are considered independent when they originate from different synoptic situations. Nine commonly-used copula types were fitted to the flood peak - flood volume pairs at each site. In this step, two databases were used: i) a process-based selection of all the independent flood events (three data samples at each catchment) and ii) the annual maxima of the flood peaks and the respective flood volumes regardless of the flood processes (one data sample per catchment). The goodness-of-fit of the nine copula types was examined on a regional basis throughout all the catchments. It was concluded that (1) the copula models for the flood processes are discernible locally; (2) the Clayton copula provides an unacceptable performance for all three processes as well as in the case of the annual maxima; (3) the rejection of the other copula types depends on the flood type and the sample size; (4) there are differences in the copulas with the best fits: for synoptic and flash floods, the best performance is associated with the extreme value copulas; for snowmelt floods, the Frank copula fits the best; while in the case of the annual maxima, no firm conclusion could be made due to the number of copulas with similarly acceptable overall performances. The general conclusion from this case study is that treating flood processes separately is beneficial; however, the usually available sample size in such real life studies is not sufficient to give generally valid recommendations for engineering design tasks. [ABSTRACT FROM AUTHOR]

This paper analyses the bivariate relationship between flood peaks and corresponding flood event volumes modelled by empirical copulas in a regional context in the North-West of Austria. Flood data of a total of 69 catchments in the region are analysed for the period 1976-2007. In order to increase the sample size and the homogeneity of the samples for the statistical analysis, 24872 hydrologically independent flood events were isolated and assigned to one of three flood process types: synoptic floods, flash floods or snowmelt floods in contrary to the more traditional engineering approach of selecting annual maxima of flood peaks and corresponding flood volumes. The first major part of the paper examines whether the empirical peak-volume copulas of different flood process types are statistically distinguishable, separately for each catchment. The results indicate that the empirical copulas of flash floods tend to be different from those of the synoptic and snowmelt floods in the target region. The second part examines how similar are the empirical flood peak-volume copulas between catchments for a given flood type. For the majority of catchment pairs, the empirical copulas of all flood types are indeed statistically similar. The flash floods show the largest degree of spatial heterogeneity. It is concluded that there is merit in treating flood types separately and in pooling events of the same type in a region when analysing and estimating flood peak-volume dependence copulas; however, the sample size of the analysed events is a limiting factor in spite of the introduced event selection procedure. [ABSTRACT FROM AUTHOR]

The main objective of the paper is to understand the contributions to the uncertainty in low-flow projections resulting from hydrological model uncertainty and climate projection uncertainty. Model uncertainty is quantified by different parameterisations of a conceptual semi-distributed hydrologic model (TUWmodel) using 11 objective functions in three different decades (1976-1986, 1987-1997, 1998- 2008), which allows for disentangling the effect of the objective function-related uncertainty and temporal stability of model parameters. Climate projection uncertainty is quantified by four future climate scenarios (ECHAM5-A1B, A2, B1 and HADCM3-A1B) using a delta change approach. The approach is tested for 262 basins in Austria. he results indicate that the seasonality of the low-flow regime is an important factor affecting the performance of model calibration in the reference period and the uncertainty of Q95 low-flow projections in the future period. In Austria, the range of simulated Q95 in the reference period is larger in basins with a summer low-flow regime than in basins with a winter low-flow regime. The accuracy of simulated Q95 may result in a range of up to 60% depending on the decade used for calibration. The low-flow projections of Q95 show an increase of low flows in the Alps, typically in the range of 10-30% and a decrease in the south-eastern part of Austria mostly in the range -5 to -20% for the climate change projected for the future period 2021-2050, relative the reference period 1978-2007. The change in seasonality varies between scenarios, but there is a tendency for earlier low flows in the northern Alps and later low flows in eastern Austria. The total uncertainty of Q95 projections is the largest in basins with a winter low-flow regime and, in some basins the range of Q95 projections exceeds 60%. In basins with summer low flows, the total uncertainty is mostly less than 20%. The ANOVA assessment of the relative contribution of the three main variance components (i.e. climate scenario, decade used for model calibration and calibration variant representing different objective function) to the low-flow projection uncertainty shows that in basins with summer low flows climate scenarios contribute more than 75% to the total projection uncertainty. In basins with a winter low-flow regime, the median contribution of climate scenario, decade and objective function is 29, 13 and 13%, respectively. The implications of the uncertainties identified in this paper for water resource management are discussed. [ABSTRACT FROM AUTHOR]

Spatial and temporal variability of snow line (SL) elevation, snow cover area (SCA) and depletion (SCD) in winters 2001-2014 is investigated in ten main Slovak river basins (the Western Carpathians). Daily satellite snow cover maps from MODIS Terra (MOD10A1, V005) and Aqua (MYD10A1, V005) with resolution 500 m are used. The results indicate three groups of basins with similar variability in the SL elevation. The first includes basins with maximum elevations above 1500 m a.s.l. (Poprad, Upper Váh, Hron, Hornád). Winter median SL is equal or close to minimum basin elevation in snow rich winters in these basins. Even in snow poor winters is SL close to the basin mean. Second group consists of mid-altitude basins with maximum elevation around 1000 m a.s.l. (Slaná, Ipeľ, Nitra, Bodrog). Median SL varies between 150 and 550 m a.s.l. in January and February, which represents approximately 40-80% snow coverage. Median SL is near the maximum basin elevation during the snow poor winters. This means that basins are in such winters snow free approximately 50% of days in January and February. The third group includes the Rudava/Myjava and Lower Váh/Danube. These basins have their maximum altitude less than 700 m a.s.l. and only a small part of these basins is covered with snow even during the snow rich winters. The evaluation of SCA shows that snow cover typically starts in December and last to February. In the highest basins (Poprad, Upper Váh), the snow season sometimes tends to start earlier (November) and lasts to March/April. The median of SCA is, however, less than 10% in these months. The median SCA of entire winter season is above 70% in the highest basins (Poprad, Upper Váh, Hron), ranges between 30-60% in the mid-altitude basins (Hornád, Slaná, Ipeľ, Nitra, Bodrog) and is less than 1% in the Myjava/Rudava and Lower Váh/Danube basins. However, there is a considerable variability in seasonal coverage between the years. Our results indicate that there is no significant trend in mean SCA in the period 2001-2014, but periods with larger and smaller SCA exist. Winters in the period 2002-2006 have noticeably larger mean SCA than those in the period 2007-2012. Snow depletion curves (SDC) do not have a simple evolution in most winters. The snowmelt tends to start between early February and the end of March. The snowmelt lasts between 8 and 15 days on average in lowland and high mountain basins, respectively. Interestingly, the variability in SDC between the winters is much larger than between the basins. [ABSTRACT FROM AUTHOR]

We analyze the controls on flood duration based on the concept of comparative hydrology. Rather than modeling a single catchment in detail, we compare catchments with contrastmg characteristics in order to understand the controls in a holistic way. We analyze the hydrographs of 9223 maximum annual flood events in 396 Austrian catchments ranging from 5 to ~10,000 km² as a function of climatic controls such as storm type (synoptic and convective storms, ram-on-snow, snowmelt), and catchment controls such as soils, soil moisture, geology, and land form. The ratio of the flood volume and the flood peak is used as a measure of the flood duration or flood timescale. The results indicate that, spatially, the median flood timescales range from 16 h in the hilly catchments, where convective storms prevail, to 104 h in the lowland catchments where substantial inundation into the floodplain occurs. The range is even larger for different flood types, from 7 h for flash floods in the hilly catchments to 200 h for snowmelt floods in an Alpine area with deeply weathered rocks and deep soils. The results also indicate that the catchment area is not the most important control on the flood timescales. For the range of catchnients considered here, climate is very important through storm type and antecedent soil moisture, and geology is very important through soil characteristics. The concept of comparative hydrology is also used to interpret the interplay of the processes controlling the flood duration at timescales from hours to millennia. It is argued that the flood timescale is a rich fingerprint of the hydrological processes in a catchment because it integrates a range of climate and catchment characteristics by a time parameter. [ABSTRACT FROM AUTHOR]

Summary: This article evaluates the spatial and temporal changes in streamflow flashiness in 122 mountain catchments in Slovakia and Austria. The flashiness is quantified by the Richards–Baker flashiness index (FI), which is the ratio of absolute day-to-day fluctuations of streamflow relative to total flow in a year. The analysis is based on daily streamflow data from the period 1976 to 2005. The results show that the average day-to-day fluctuations of streamflow vary from 6% to 43%, depending on the catchment. The spatial pattern of the FI reflects the variations in the main geological units and generally shows a trend of decreasing flashiness with increasing size of the catchment. Statistically significant temporal trends in flashiness are found in 7 Slovak and 22 Austrian catchments. Most of these trends are related to anthropogenic effects, while, in a few catchments, the change in annual flashiness appears to be caused by changes in precipitation seasonality. A multivariate statistical analysis of FI indicates negative correlations with catchment area, mean catchment elevation, percents of forest cover, agricultural land and Quaternary geology. Positive correlations are found between FI and Tertiary and Calcareous geologies. Extrapolating the regression models beyond the observed range of catchment attributes used in the estimation leads to significant prediction errors. In order to better interpret the FI values, a statistically significant relationship was found between the FI and the frequency of peak flows exceeding the long-term mean as well as between the FI and the 5% quantile of daily streamflow. [Copyright &y& Elsevier]

Climate impact analyses are usually based on driving hydrological models by future climate scenarios, assuming that the model parameters calibrated to past runoff are representative of the future. In this paper we calibrate the parameters of a conceptual rainfall-runoff model to six consecutive 5 year periods between 1976 and 2006 for 273 catchments in Austria and analyze the temporal change of the calibrated parameters. The calibrated parameters representing snow and soil moisture processes show significant trends. For example, the parameter controlling runoff generation doubled, on average, in the 3 decades. Comparisons of different subregions, comparisons with independent data sets, and analyses of the spatial variability of the model parameters indicate that these trends represent hydrological changes rather than calibration artifacts. The trends can be related to changes in the climatic conditions of the catchments such as higher evapotranspiration and drier catchment conditions in the more recent years. The simulations suggest that the impact on simulated runoff of assuming time invariant parameters can be very significant. For example, if using the parameters calibrated to 1976-1981 for simulating runoff for the period 2001-2006, the biases of median flows are, on average, 15% and the biases of high flows are about 35%. The errors increase as the time lag between the simulation and calibration periods increases. The implications for hydrologic prediction in general and climate impact analyses in particular are discussed. [ABSTRACT FROM AUTHOR]

Studying the changes in extreme river runoff induced by climate change is of utmost importance, as the variability of floods directly affects life and human activities. This study examines the fluctuations and persistence of winter floods in 14 catchments in the Rika River Basin (Ukraine) and ten catchments in the Steyr River Basin (Austria). The catchments represent typical hydrological regimes in the Danube River region. The fluctuations and persistence of floods are analyzed by the hydro-genetic method and a seasonality analysis for the period 1951–2015. The results show a much more pronounced fluctuation pattern in the upper Rika catchments than in the upper Steyr catchments. This pattern indicates an increase in winter flood magnitudes between the mid-1960s and the 1990s, followed by a decrease until recently. The flood seasonality shows a large inter-annual variability in both regions. The most significant winter floods tend to occur in November and December. The winter flood fluctuations are compared with changes in associated climate characteristics, i.e., seven-day maximum precipitation, a melt index, and annual maximum snow depth. The seasonality of these characteristics has a strong inter-annual variability and only partly explains the winter flood fluctuations. [ABSTRACT FROM AUTHOR]

The space time variability of flood events in agricultural landscape is controlled by complex interactions between atmospheric, soil and land use characteristics. The main objective of this study is to investigate the factors controlling spatial and temporal variability of event runoff coefficient (Rc) and recesstion time constant (Tc) in Hydroloigcal Open Air Laboratory (HOAL), Petzenkirchen, Austria. HOAL is a small agricultural catchment (66ha) where runoff is measured in four different runoff generation systems, i.e. wetland, tile drainage, natural drainage systems and the main outlet. The analysis is based on 57 runoff events identified in the period 2013 to 2015. The event runoff characteristics are estimated by using methodology proposed by Merz et al. (2006). Results show that event runoff coefficients in HOAL are generally small. The largest event runoff coefficients are observed in the tile drainage systems (average Rc equals 0.09), in the wetland and natural systems is the average runoff coefficient 0.03 and 0.05 respectively. The mean Tc is the largest (6.7 hour) for the main outlet as it is associated with multiple sources and flow paths. Both Rc and Tc have a clear seasonal signal characterized by smaller values in summer and larger values in winter. The exception is observed in the wetlands, which have very small seasonal dynamics of Rc and Tc. The factors controlling the spatial and temporal variability of event runoff characteristics are identified and evaluated by using three stochastic regression algorithms, i.e. Random forest; Gradient Boost Decision Tree and Support vector machine. The results show that Support vector machine algorithm has better prediction performance compared to the other two types of methods. The most important factor explaining the temporal variability of Rc and Tc is the duration of precipitation and magnitude of precipitation peak. While antecedent soil moisture has some impact only for estimation of Rc dynamics in natural drainage systems, the areal coverage of pipes is an important factor for estimation of Rc in the tile drainage systems. [ABSTRACT FROM AUTHOR]

• A framework developed for investigating cumulative impact of damming on water. • Tibetan damming having serious downstream impact but limited to local scale. • Runoff increase under warmer climate most likely offsetting damming impact. • This framework can be applicable for various anthropogenic impact analysis. Damming is one of the major methods that human being secure reliable freshwater. It can change flow magnitude and regime along a river, particularly for Transboundary Rivers shared by multiple jurisdictions. This study develops a coupled framework to assess both the current and future cumulative impacts of damming on downstream water availability for data-sparse regions like the Tibetan Plateau. The framework is applied to the Brahmaputra River Basin (BRB), where dam developments on the Tibetan Plateau are highly concerned. The results show that, despite being part of the 'Asian water tower', the Yarlung Tsangpo River (YTR) contributes just 15–16% to Brahmaputra streamflow, for 48% of its contributing area. We found that the maximum impact due to five recently constructed dams in YTR suggests about 8–11% reduction of mean annual streamflow at outlet of Brahmaputra River. The impacts of damming would be largely offset by runoff increases under the climate scenarios projected by 28 CMIP5 model outputs from RCP4.5 median-level emission scenarios and 29 CMIP5 model outputs from RCP8.5 high-level emission scenarios for the period of 2049 to 2075, compared to the historical period of 1979 to 2005. Our results suggest that it is necessary to use our framework or a similar one to comprehensively evaluate the maximum possible damming impact across the Transboundary Rivers. [ABSTRACT FROM AUTHOR]