Refine
Year of publication
Document Type
Keywords
- Connectivity (4)
- Precipitation (3)
- runoff (3)
- Brazil (2)
- Climate (2)
- Forecasting Framework (2)
- IMPRESSIONS (2)
- Jaguaribe Basin (2)
- Nordeste (2)
- Reservoir Networks (2)
- SWIM (2)
- Samara (2)
- Sediment Transport (2)
- Teteriv (2)
- Ukraine (2)
- Uncertainty Processor (2)
- Water Availability (2)
- Western Bug (2)
- alpine (2)
- catchments (2)
- climate change (2)
- climate change impact (2)
- hydroclimatology (2)
- hydrological modelling (2)
- impacts (2)
- natural hazards (2)
- regimes (2)
- river discharge (2)
- seasonality (2)
- semi-arid hydrology (2)
- snow (2)
- streamflow (2)
- switzerland (2)
- temperature (2)
- time-series (2)
- trend detection (2)
- variability (2)
- Alps (1)
- Amazon region (1)
- Baltic Sea Coast (1)
- Best management practice (1)
- Coastal regions (1)
- Complex terrain (1)
- Cost-benefit (1)
- Deposition (1)
- ECHSE (1)
- Erosion (1)
- Extreme discharge data (1)
- Extreme event (1)
- Extremniederschlag (1)
- Flash flood analysis (1)
- Flood forecasting (1)
- Forensic disaster analysis (1)
- Genetic algorithm (1)
- Hangrutschungen (1)
- Hydrological modelling (1)
- Hydrology (1)
- Image classification (1)
- Inverse methods (1)
- Isabena river (1)
- Lake Malawi basin (1)
- Low impact development (1)
- MUSLE (1)
- Machine learning (1)
- Mann-Kendall test (1)
- Mixing models (1)
- Modelling (1)
- Mountain meteorology (1)
- Multi-angular model-based decomposition (1)
- NSGA-II (1)
- Naturgefahren (1)
- Nordic catchments (1)
- Northeast Spain (1)
- Radar rainfall data (1)
- Remote sensing (1)
- Reservoir network (1)
- Reservoirs (1)
- SPEI (1)
- SPI (1)
- Schadensabschätzung (1)
- Sediment fingerprinting (1)
- Sediment redistribution (1)
- Sediment retention (1)
- Sediment transfer (1)
- Semi-arid (1)
- Semiarid (1)
- Semiarid catchment (1)
- Shire River basin (1)
- Soil moisture (1)
- Soil moisture measurement comparison (1)
- South America (1)
- Spatial scale (1)
- Spectroscopy (1)
- Storm water management model (1)
- Streamflow (1)
- Sturzflut (1)
- Suspended sediment (1)
- USLE (1)
- WASA-SED (1)
- Water budget / balance (1)
- Water storage dynamic (1)
- alpine catchments (1)
- catchment (1)
- catchment scale (1)
- channel transmission losses (1)
- coastal wetland (1)
- connectivity (1)
- damage assessment (1)
- differential split-sample test (1)
- drainage of the catchment area (1)
- dryland rivers (1)
- drylands (1)
- ecohydrological modelling (1)
- erosion (1)
- experimental catchments (1)
- extreme precipitation (1)
- feedback (1)
- fens (1)
- flash flood (1)
- flexible model (1)
- flood generating processes (1)
- flood seasonality (1)
- floods (1)
- geomorphology (1)
- global change (1)
- groundwater dynamics (1)
- groundwater flow (1)
- hydrological drought (1)
- hyporheic zone (1)
- identifiability analysis (1)
- impoundment rate (1)
- infiltration (1)
- integrated modelling (1)
- integrated river basin management (1)
- integrated river basin modelling (1)
- landslides (1)
- macropore flow (1)
- management effects (1)
- meso-scale ecosystems (1)
- meteorological drought (1)
- model enhancement (1)
- model structure (1)
- modeling (1)
- modelling (1)
- monitoring (1)
- mountainous rivers (1)
- multi-temporal RapidEye satellite data (1)
- multiyear drought (1)
- north-eastern Brazil (1)
- numerics (1)
- nutrient retention (1)
- parametric and nonparametric comparison (1)
- plant-animal-soil-system (1)
- polarimetric SAR (1)
- process based (1)
- radar imaging (1)
- rainfall simulation (1)
- remote sensing (1)
- riparian zones (1)
- river networks (1)
- riveraquifer interaction (1)
- sediment (1)
- semi-arid area (1)
- sensitivity analyses (1)
- soil moisture (1)
- stochastic dynamical systems (1)
- streamflow probabilistic forecasting (1)
- suspended sediments (1)
- synthetic aperture radar (SAR) (1)
- system analysis (1)
- time series analysis (1)
- trend analysis (1)
- trend attribution (1)
- trend drivers (1)
- water balance (1)
- water demand (1)
- water management (1)
- water quality (1)
- water resources management (1)
- wetlands (1)
Institute
- Institut für Geowissenschaften (67) (remove)
This study presents an application of an innovative sampling strategy to assess soil moisture dynamics in a headwater of the Weisseritz in the German eastern Ore Mountains. A grassland site and a forested site were instrumented with two Spatial TDR clusters (STDR) that consist of 39 and 32 coated TDR probes of 60 cm length. Distributed time series of vertically averaged soil moisture data from both sites/ensembles were analyzed by statistical and geostatistical methods. Spatial variability and the spatial mean at the forested site were larger than at the grassland site. Furthermore, clustering of TDR probes in combination with long-term monitoring allowed identification of average spatial covariance structures at the small field scale for different wetness states. The correlation length of soil water content as well as the sill to nugget ratio at the grassland site increased with increasing average wetness and but, in contrast, were constant at the forested site. As soil properties at both the forested and grassland sites are extremely variable, this suggests that the correlation structure at the forested site is dominated by the pattern of throughfall and interception. We also found a very strong correlation between antecedent soil moisture at the forested site and runoff coefficients of rainfall-runoff events observed at gauge Rehefeld. Antecedent soil moisture at the forest site explains 92% of the variability in the runoff coefficients. By combining these results with a recession analysis we derived a first conceptual model of the dominant runoff mechanisms operating in this catchment. Finally, we employed a physically based hydrological model to shed light on the controls of soil- and plant morphological parameters on soil average soil moisture at the forested site and the grassland site, respectively. A homogeneous soil setup allowed, after fine tuning of plant morphological parameters, most of the time unbiased predictions of the observed average soil conditions observed at both field sites. We conclude that the proposed sampling strategy of clustering TDR probes is suitable to assess unbiased average soil moisture dynamics in critical functional units, in this case the forested site, which is a much better predictor for event scale runoff formation than pre-event discharge. Long term monitoring of such critical landscape elements could maybe yield valuable information for flood warning in headwaters. We thus think that STDR provides a good intersect of the advantages of permanent sampling and spatially highly resolved soil moisture sampling using mobile rods.
Scenario-neutral response surfaces illustrate the sensitivity of a simulated natural system, represented by a specific impact variable, to systematic perturbations of climatic parameters. This type of approach has recently been developed as an alternative to top-down approaches for the assessment of climate change impacts. A major limitation of this approach is the underrepresentation of changes in the temporal structure of the climate input data (i.e., the seasonal and day-to-day variability) since this is not altered by the perturbation. This paper presents a framework that aims to examine this limitation by perturbing both observed and projected climate data time series for a future period, which both serve as input into a hydrological model (the HBV model). The resulting multiple response surfaces are compared at a common domain, the standardized runoff response surface (SRRS). We apply this approach in a case study catchment in Norway to (i) analyze possible changes in mean and extreme runoff and (ii) quantify the influence of changes in the temporal structure represented by 17 different climate input sets using linear mixed-effect models. Results suggest that climate change induced increases in mean and peak flow runoff and only small changes in low flow. They further suggest that the effect of the different temporal structures of the climate input data considerably affects low flows and floods (at least 21% influence), while it is negligible for mean runoff.
Climate change is likely to impact the seasonality and generation processes of floods in the Nordic countries, which has direct implications for flood risk assessment, design flood estimation, and hydropower production management. Using a multi-model/multi-parameter approach to simulate daily discharge for a reference (1961–1990) and a future (2071–2099) period, we analysed the projected changes in flood seasonality and generation processes in six catchments with mixed snowmelt/rainfall regimes under the current climate in Norway. The multi-model/multi-parameter ensemble consists of (i) eight combinations of global and regional climate models, (ii) two methods for adjusting the climate model output to the catchment scale, and (iii) one conceptual hydrological model with 25 calibrated parameter sets. Results indicate that autumn/winter events become more frequent in all catchments considered, which leads to an intensification of the current autumn/winter flood regime for the coastal catchments, a reduction of the dominance of spring/summer flood regimes in a high-mountain catchment, and a possible systematic shift in the current flood regimes from spring/summer to autumn/winter in the two catchments located in northern and south-eastern Norway. The changes in flood regimes result from increasing event magnitudes or frequencies, or a combination of both during autumn and winter. Changes towards more dominant autumn/winter events correspond to an increasing relevance of rainfall as a flood generating process (FGP) which is most pronounced in those catchments with the largest shifts in flood seasonality. Here, rainfall replaces snowmelt as the dominant FGP primarily due to increasing temperature.We further analysed the ensemble components in contributing to overall uncertainty in the projected changes and found that the climate
projections and the methods for downscaling or bias correction tend to be the largest contributors. The relative role of hydrological parameter uncertainty, however, is highest for those catchments showing the largest changes in flood seasonality, which confirms the lack of robustness in hydrological model parameterization for simulations under transient hydrometeorological conditions.
Climate change is likely to impact the seasonality and generation processes of floods in the Nordic countries, which has direct implications for flood risk assessment, design flood estimation, and hydropower production management. Using a multi-model/multi-parameter approach to simulate daily discharge for a reference (1961–1990) and a future (2071–2099) period, we analysed the projected changes in flood seasonality and generation processes in six catchments with mixed snowmelt/rainfall regimes under the current climate in Norway. The multi-model/multi-parameter ensemble consists of (i) eight combinations of global and regional climate models, (ii) two methods for adjusting the climate model output to the catchment scale, and (iii) one conceptual hydrological model with 25 calibrated parameter sets. Results indicate that autumn/winter events become more frequent in all catchments considered, which leads to an intensification of the current autumn/winter flood regime for the coastal catchments, a reduction of the dominance of spring/summer flood regimes in a high-mountain catchment, and a possible systematic shift in the current flood regimes from spring/summer to autumn/winter in the two catchments located in northern and south-eastern Norway. The changes in flood regimes result from increasing event magnitudes or frequencies, or a combination of both during autumn and winter. Changes towards more dominant autumn/winter events correspond to an increasing relevance of rainfall as a flood generating process (FGP) which is most pronounced in those catchments with the largest shifts in flood seasonality. Here, rainfall replaces snowmelt as the dominant FGP primarily due to increasing temperature.We further analysed the ensemble components in contributing to overall uncertainty in the projected changes and found that the climate projections and the methods for downscaling or bias correction tend to be the largest contributors. The relative role of hydrological parameter uncertainty, however, is highest for those catchments showing the largest changes in flood seasonality, which confirms the lack of robustness in hydrological model parameterization for simulations under transient hydrometeorological conditions.
This paper investigates the transferability of calibrated HBV model parameters under stable and contrasting conditions in terms of flood seasonality and flood generating processes (FGP) in five Norwegian catchments with mixed snowmelt/rainfall regimes. We apply a series of generalized (differential) split-sample tests using a 6-year moving window over (i) the entire runoff observation periods, and (ii) two subsets of runoff observations distinguished by the seasonal occurrence of annual maximum floods during either spring or autumn. The results indicate a general model performance loss due to the transfer of calibrated parameters to independent validation periods of -5 to -17%, on average. However, there is no indication that contrasting flood seasonality exacerbates performance losses, which contradicts the assumption that optimized parameter sets for snowmelt-dominated floods (during spring) perform particularly poorly on validation periods with rainfall-dominated floods (during autumn) and vice versa.
This paper reports on a project to compare predictions from a range of catchment models applied to a mesoscale river basin in central Germany and to assess various ensemble predictions of catchment streamflow. The models encompass a large range in inherent complexity and input requirements. In approximate order of decreasing complexity, they are DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP, HBV, LASCAM and IHACRES. The models are calibrated twice using different sets of input data. The two predictions from each model are then combined by simple averaging to produce a single-model ensemble. The 10 resulting single-model ensembles are combined in various ways to produce multi-model ensemble predictions. Both the single-model ensembles and the multi-model ensembles are shown to give predictions that are generally superior to those of their respective constituent models, both during a 7-year calibration period and a 9- year validation period. This occurs despite a considerable disparity in performance of the individual models. Even the weakest of models is shown to contribute useful information to the ensembles they are part of. The best model combination methods are a trimmed mean (constructed using the central four or six predictions each day) and a weighted mean ensemble (with weights calculated from calibration performance) that places relatively large weights on the better performing models. Conditional ensembles. in which separate model weights are used in different system states (e.g. summer and winter, high and low flows) generally yield little improvement over the weighted mean ensemble. However a conditional ensemble that discriminates between rising and receding flows shows moderate improvement. An analysis of ensemble predictions shows that the best ensembles are not necessarily those containing the best individual models. Conversely, it appears that some models that predict well individually do not necessarily combine well with other models in multi-model ensembles. The reasons behind these observations may relate to the effects of the weighting schemes, non- stationarity of the climate series and possible cross-correlations between models.
Subsurface stormflow is thought to occur mainly in humid environments with steep terrains. However, in semi-arid areas, preferential flow through macropores can also result in a significant contribution of subsurface stormflow to catchment runoff for varying catchment conditions. Most hydrological models neglect this important subsurface preferential flow. Here, we use the process-oriented hydrological model Hillflow-3D, which includes a macropore flow approach, to simulate rainfall-runoff in the semi-arid Parapunos catchment in Spain, where macropore flow was observed in previous research. The model was extended for this study to account for sorptivity under very dry soil conditions. The results of the model simulations with and without macropore flow are compared. Both model versions give reasonable results for average rainfall situations, although the approach with the macropore concept provides slightly better results. The model results for scenarios of extreme rainfall events (>13.3mm30min(-1)) however show large differences between the versions with and without macropores. These model results compared with measured rainfall-runoff data show that the model with the macropore concept is better. Our conclusion is that preferential flow is important in controlling surface runoff in case of specific, high intensity rainfall events. Therefore, preferential flow processes must be included in hydrological models where we know that preferential flow occurs. Hydrological process models with a less detailed process description may fit observed average events reasonably well but can result in erroneous predictions for more extreme events. Copyright (c) 2013 John Wiley & Sons, Ltd.
Knowledge on the response of sediment export to recent climate change in glacierized areas in the European Alps is limited, primarily because long-term records of suspended sediment concentrations (SSCs) are scarce. Here we tested the estimation of sediment export of the past five decades using quantile regression forest (QRF), a nonparametric, multivariate regression based on random forest. The regression builds on short-term records of SSCs and long records of the most important hydroclimatic drivers (discharge, precipitation and air temperature - QPT). We trained independent models for two nested and partially glacier-covered catchments, Vent (98 km(2)) and Vernagt (11.4 km(2)), in the upper otztal in Tyrol, Austria (1891 to 3772 m a.s.l.), where available QPT records start in 1967 and 1975. To assess temporal extrapolation ability, we used two 2-year SSC datasets at gauge Vernagt, which are almost 20 years apart, for a validation. For Vent, we performed a five-fold cross-validation on the 15 years of SSC measurements. Further, we quantified the number of days where predictors exceeded the range represented in the training dataset, as the inability to extrapolate beyond this range is a known limitation of QRF. Finally, we compared QRF performance to sediment rating curves (SRCs). We analyzed the modeled sediment export time series, the predictors and glacier mass balance data for trends (Mann-Kendall test and Sen's slope estimator) and step-like changes (using the widely applied Pettitt test and a complementary Bayesian approach).Our validation at gauge Vernagt demonstrated that QRF performs well in estimating past daily sediment export (Nash-Sutcliffe efficiency (NSE) of 0.73) and satisfactorily for SSCs (NSE of 0.51), despite the small training dataset. The temporal extrapolation ability of QRF was superior to SRCs, especially in periods with high-SSC events, which demonstrated the ability of QRF to model threshold effects. Days with high SSCs tended to be underestimated, but the effect on annual yields was small. Days with predictor exceedances were rare, indicating a good representativity of the training dataset. Finally, the QRF reconstruction models outperformed SRCs by about 20 percent points of the explained variance.Significant positive trends in the reconstructed annual suspended sediment yields were found at both gauges, with distinct step-like increases around 1981. This was linked to increased glacier melt, which became apparent through step-like increases in discharge at both gauges as well as change points in mass balances of the two largest glaciers in the Vent catchment. We identified exceptionally high July temperatures in 1982 and 1983 as a likely cause. In contrast, we did not find coinciding change points in precipitation. Opposing trends at the two gauges after 1981 suggest different timings of "peak sediment". We conclude that, given large-enough training datasets, the presented QRF approach is a promising tool with the ability to deepen our understanding of the response of high-alpine areas to decadal climate change.
In this paper, we analyse the effectiveness of flood management measures based on the concept known as "retaining water in the landscape". The investigated measures include afforestation, micro-ponds and small-reservoirs. A comparative and model-based methodological approach has been developed and applied for three meso-scale catchments located in different European hydro-climatological regions: Poyo (184 km(2)) in the Spanish Mediterranean, Upper Iller (954 km(2)) in the German Alps and Kamp (621 km(2)) in Northeast-Austria representing the Continental hydro-climate. This comparative analysis has found general similarities in spite of the particular differences among studied areas. In general terms, the flood reduction through the concept of "retaining water in the landscape" depends on the following factors: the storage capacity increase in the catchment resulting from such measures, the characteristics of the rainfall event, the antecedent soil moisture condition and the spatial distribution of such flood management measures in the catchment. In general, our study has shown that, this concept is effective for small and medium events, but almost negligible for the largest and less frequent floods: this holds true for all different hydro-climatic regions, and with different land-use, soils and morphological settings.
Climatic change alters the frequency and intensity of natural hazards. In order to assess potential future changes in flood seasonality in the Rhine River Basin, we analyse changes in streamflow, snowmelt, precipitation, and evapotranspiration at 1.5, 2.0 and 3.0 ◦C global warming levels. The mesoscale Hydrological Model (mHM) forced with an ensemble of climate projection scenarios (five general circulation models under three representative concentration pathways) is used to simulate the present and future climate conditions of both, pluvial and nival hydrological regimes. Our results indicate that the interplay between changes in snowmelt- and rainfall-driven runoff is crucial to understand changes in streamflow maxima in the Rhine River. Climate projections suggest that future changes in flood characteristics in the entire Rhine River are controlled by both, more intense precipitation events and diminishing snow packs. The nature of this interplay defines the type of change in runoff peaks. On the sub-basin level (the Moselle River), more intense rainfall during winter is mostly counterbalanced by reduced snowmelt contribution to the streamflow. In the High Rhine (gauge at Basel), the strongest increases in streamflow maxima show up during winter, when strong increases in liquid precipitation intensity encounter almost unchanged snowmelt-driven runoff. The analysis of snowmelt events suggests that at no point in time during the snowmelt season, a warming climate results in an increase in the risk of snowmelt-driven flooding. We do not find indications of a transient merging of pluvial and nival floods due to climate warming.