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Extreme Regenereignisse von kurzer Dauer im Bereich von Stunden und darunter rücken aufgrund der dadurch bedingten Schäden durch Sturzfluten und auch wegen ihrer möglichen Intensivierungen durch den anthropogenen Klimawandel immer stärker in den Fokus. Die vorliegende Studie untersucht auf Basis von teilweise sehr langen (> 50 Jahre) und zeitlich hochaufgelösten Zeitreihen (≤ 15 Minuten) mögliche Trends in Starkregenintensitäten für Stationen aus schweizerischen und österreichischen Alpenregionen sowie für das Emscher-Lippe-Gebiet in Nordrhein-Westfalen. Es wird deutlich, dass es eine Zunahme der extremen Niederschlagsintensitäten gibt, welche gut durch die Erwärmung des regionalen Klimas erklärt werden kann: Die Analysen langfristiger Trends der Überschreitungssummen und Wiederkehrniveaus zeigen zwar erhebliche Unsicherheiten, lassen jedoch eine Zunahme in einer Größenordnung von 30 % pro Jahrhundert erkennen. Zudem wird in diesem Beitrag, basierend auf einer "mittleren" Klimasimulation für das 21. Jahrhundert, für ausgewählte Stationen der Emscher-Lippe-Region eine Projektion für extreme Niederschlagsintensitäten in sehr hoher zeitlicher Auflösung beschrieben. Dabei wird ein gekoppeltes räumliches und zeitliches "Downscaling" angewendet, dessen entscheidende Neuerung die Berücksichtigung der Abhängigkeit der lokalen Regenintensität von der Lufttemperatur ist. Dieses Verfahren beinhaltet zwei Schritte: Zuerst werden großräumige Klimafelder in täglicher Auflösung durch Regression mit den Temperatur- und Niederschlagswerten der Stationen statistisch verbunden (räumliches Downscaling). Im zweiten Schritt werden dann diese Stationswerte mithilfe eines sogenannten multiplikativen stochastischen Kaskadenmodells (MC) auf eine zeitliche Auflösung von 10 Minuten disaggregiert (zeitliches Downscaling). Die neuartige, temperatursensitive Variante berücksichtigt zusätzlich die Lufttemperatur als erklärende Variable für die Niederschlagsintensitäten. Dadurch wird der mit einer Erwärmung zu erwartende höhere atmosphärische Feuchtegehalt, welcher sich aus der Clausius-Clapeyron-Beziehung (CC) ergibt, mit in das zeitliche Downscaling einbezogen.
Für die statistische Auswertung der extremen kurzfristigen Niederschläge wurden die oberen Quantile (99,9 %), Überschreitungssummen (ÜS, P > 5 mm) und 3-jährliche Wiederkehrniveaus (WN) einer Dauerstufe von ≤ 15-Minuten betrachtet. Diese Auswahl erlaubt die gleichzeitige Analyse sowohl von Extremwertstatistiken als auch von deren langfristigen Trends; leichte Abweichungen von dieser Wahl beeinflussen die Hauptergebnisse nur unwesentlich. Nur durch die Hinzunahme der Temperatur wird die beobachtete Temperaturabhängigkeit der extremen Quantile (CC-Scaling) gut wiedergegeben. Bei Vergleich von Beobachtungsdaten und Gegenwartssimulationen der Modellkaskade zeigt das temperatursensitive Verfahren konsistente Ergebnisse. Im Vergleich zu den Entwicklungen der letzten Jahrzehnte werden für die Zukunft ähnliche oder sogar noch stärkere Anstiege der extremen Niederschlagsintensitäten projiziert. Dies ist insofern bemerkenswert, als diese anscheinend hauptsächlich durch die örtliche Temperatur bestimmt werden, denn die projizierten Trends der Niederschlags-Tageswerte sind für diese Region vernachlässigbar.
Veränderung der Abflüsse
(2005)
From 6 to 9 August 2012, intense rainfall hit the northern Philippines, causing massive floods in Metropolitan Manila and nearby regions. Local rain gauges recorded almost 1000mm within this period. However, the recently installed Philippine network of weather radars suggests that Metropolitan Manila might have escaped a potentially bigger flood just by a whisker, since the centre of mass of accumulated rainfall was located over Manila Bay. A shift of this centre by no more than 20 km could have resulted in a flood disaster far worse than what occurred during Typhoon Ketsana in September 2009.
Spatial patterns as well as temporal dynamics of soil moisture have a major influence on runoff generation. The investigation of these dynamics and patterns can thus yield valuable information on hydrological processes, especially in data scarce or previously ungauged catchments. The combination of spatially scarce but temporally high resolution soil moisture profiles with episodic and thus temporally scarce moisture profiles at additional locations provides information on spatial as well as temporal patterns of soil moisture at the hillslope transect scale. This approach is better suited to difficult terrain (dense forest, steep slopes) than geophysical techniques and at the same time less cost-intensive than a high resolution grid of continuously measuring sensors. Rainfall simulation experiments with dye tracers while continuously monitoring soil moisture response allows for visualization of flow processes in the unsaturated zone at these locations. Data was analyzed at different spacio-temporal scales using various graphical methods, such as space-time colour maps (for the event and plot scale) and binary indicator maps (for the long-term and hillslope scale). Annual dynamics of soil moisture and decimeterscale variability were also investigated. The proposed approach proved to be successful in the investigation of flow processes in the unsaturated zone and showed the importance of preferential flow in the Malalcahuello Catchment, a datascarce catchment in the Andes of Southern Chile. Fast response times of stream flow indicate that preferential flow observed at the plot scale might also be of importance at the hillslope or catchment scale. Flow patterns were highly variable in space but persistent in time. The most likely explanation for preferential flow in this catchment is a combination of hydrophobicity, small scale heterogeneity in rainfall due to redistribution in the canopy and strong gradients in unsaturated conductivities leading to self-reinforcing flow paths.
Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany
(2023)
Data-driven models have been recently suggested to surrogate computationally expensive hydrodynamic models to map flood hazards. However, most studies focused on developing models for the same area or the same precipitation event. It is thus not obvious how transferable the models are in space. This study evaluates the performance of a convolutional neural network (CNN) based on the U-Net architecture and the random forest (RF) algorithm to predict flood water depth, the models' transferability in space and performance improvement using transfer learning techniques. We used three study areas in Berlin to train, validate and test the models. The results showed that (1) the RF models outperformed the CNN models for predictions within the training domain, presumable at the cost of overfitting; (2) the CNN models had significantly higher potential than the RF models to generalize beyond the training domain; and (3) the CNN models could better benefit from transfer learning technique to boost their performance outside training domains than RF models.
Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany
(2023)
Data-driven models have been recently suggested to surrogate computationally expensive hydrodynamic models to map flood hazards. However, most studies focused on developing models for the same area or the same precipitation event. It is thus not obvious how transferable the models are in space. This study evaluates the performance of a convolutional neural network (CNN) based on the U-Net architecture and the random forest (RF) algorithm to predict flood water depth, the models' transferability in space and performance improvement using transfer learning techniques. We used three study areas in Berlin to train, validate and test the models. The results showed that (1) the RF models outperformed the CNN models for predictions within the training domain, presumable at the cost of overfitting; (2) the CNN models had significantly higher potential than the RF models to generalize beyond the training domain; and (3) the CNN models could better benefit from transfer learning technique to boost their performance outside training domains than RF models.
Identifying urban pluvial flood-prone areas is necessary but the application of two-dimensional hydrodynamic models is limited to small areas. Data-driven models have been showing their ability to map flood susceptibility but their application in urban pluvial flooding is still rare. A flood inventory (4333 flooded locations) and 11 factors which potentially indicate an increased hazard for pluvial flooding were used to implement convolutional neural network (CNN), artificial neural network (ANN), random forest (RF) and support vector machine (SVM) to: (1) Map flood susceptibility in Berlin at 30, 10, 5, and 2 m spatial resolutions. (2) Evaluate the trained models' transferability in space. (3) Estimate the most useful factors for flood susceptibility mapping. The models' performance was validated using the Kappa, and the area under the receiver operating characteristic curve (AUC). The results indicated that all models perform very well (minimum AUC = 0.87 for the testing dataset). The RF models outperformed all other models at all spatial resolutions and the RF model at 2 m spatial resolution was superior for the present flood inventory and predictor variables. The majority of the models had a moderate performance for predictions outside the training area based on Kappa evaluation (minimum AUC = 0.8). Aspect and altitude were the most influencing factors on the image-based and point-based models respectively. Data-driven models can be a reliable tool for urban pluvial flood susceptibility mapping wherever a reliable flood inventory is available.
Identifying urban pluvial flood-prone areas is necessary but the application of two-dimensional hydrodynamic models is limited to small areas. Data-driven models have been showing their ability to map flood susceptibility but their application in urban pluvial flooding is still rare. A flood inventory (4333 flooded locations) and 11 factors which potentially indicate an increased hazard for pluvial flooding were used to implement convolutional neural network (CNN), artificial neural network (ANN), random forest (RF) and support vector machine (SVM) to: (1) Map flood susceptibility in Berlin at 30, 10, 5, and 2 m spatial resolutions. (2) Evaluate the trained models' transferability in space. (3) Estimate the most useful factors for flood susceptibility mapping. The models' performance was validated using the Kappa, and the area under the receiver operating characteristic curve (AUC). The results indicated that all models perform very well (minimum AUC = 0.87 for the testing dataset). The RF models outperformed all other models at all spatial resolutions and the RF model at 2 m spatial resolution was superior for the present flood inventory and predictor variables. The majority of the models had a moderate performance for predictions outside the training area based on Kappa evaluation (minimum AUC = 0.8). Aspect and altitude were the most influencing factors on the image-based and point-based models respectively. Data-driven models can be a reliable tool for urban pluvial flood susceptibility mapping wherever a reliable flood inventory is available.
Most hydrological studies rely on a model calibrated using discharge alone. However, judging the model reliability based on such calibration is problematic, as it does not guarantee the correct representation of internal hydrological processes. This study aims (a) to develop a comprehensive multi-objective calibration framework using remote sensing vegetation data and hydrological signatures (flow duration curve - FDC, and baseflow index) in addition to discharge, and (b) to apply this framework for calibration of the Soil and Water Assessment Tool (SWAT) in a typical Andean catchment. Overall, our calibration approach outperformed traditional discharge-based and FDC signature-based calibration strategies in terms of vegetation, streamflow, and flow partitioning simulation. New hydrological insights for the region are the following: baseflow is the main component of the streamflow sustaining the long dry-season flow, and pasture areas offer higher water yield and baseflow than other land-cover types. The proposed approach could be used in other data-scarce regions with complex topography.
Most hydrological studies rely on a model calibrated using discharge alone. However, judging the model reliability based on such calibration is problematic, as it does not guarantee the correct representation of internal hydrological processes. This study aims (a) to develop a comprehensive multi-objective calibration framework using remote sensing vegetation data and hydrological signatures (flow duration curve - FDC, and baseflow index) in addition to discharge, and (b) to apply this framework for calibration of the Soil and Water Assessment Tool (SWAT) in a typical Andean catchment. Overall, our calibration approach outperformed traditional discharge-based and FDC signature-based calibration strategies in terms of vegetation, streamflow, and flow partitioning simulation. New hydrological insights for the region are the following: baseflow is the main component of the streamflow sustaining the long dry-season flow, and pasture areas offer higher water yield and baseflow than other land-cover types. The proposed approach could be used in other data-scarce regions with complex topography.
The study examined the potential future changes of drought characteristics in the Greater Lake Malawi Basin in Southeast Africa. This region strongly depends on water resources to generate electricity and food. Future projections (considering both moderate and high emission scenarios) of temperature and precipitation from an ensemble of 16 bias-corrected climate model combinations were blended with a scenario-neutral response surface approach to analyses changes in: (i) the meteorological conditions, (ii) the meteorological water balance, and (iii) selected drought characteristics such as drought intensity, drought months, and drought events, which were derived from the Standardized Precipitation and Evapotranspiration Index. Changes were analyzed for a near-term (2021-2050) and far-term period (2071-2100) with reference to 1976-2005. The effect of bias-correction (i.e., empirical quantile mapping) on the ability of the climate model ensemble to reproduce observed drought characteristics as compared to raw climate projections was also investigated. Results suggest that the bias-correction improves the climate models in terms of reproducing temperature and precipitation statistics but not drought characteristics. Still, despite the differences in the internal structures and uncertainties that exist among the climate models, they all agree on an increase of meteorological droughts in the future in terms of higher drought intensity and longer events. Drought intensity is projected to increase between +25 and +50% during 2021-2050 and between +131 and +388% during 2071-2100. This translates into +3 to +5, and +7 to +8 more drought months per year during both periods, respectively. With longer lasting drought events, the number of drought events decreases. Projected droughts based on the high emission scenario are 1.7 times more severe than droughts based on the moderate scenario. That means that droughts in this region will likely become more severe in the coming decades. Despite the inherent high uncertainties of climate projections, the results provide a basis in planning and (water-)managing activities for climate change adaptation measures in Malawi. This is of particular relevance for water management issues referring hydro power generation and food production, both for rain-fed and irrigated agriculture.
The study examined the potential future changes of drought characteristics in the Greater Lake Malawi Basin in Southeast Africa. This region strongly depends on water resources to generate electricity and food. Future projections (considering both moderate and high emission scenarios) of temperature and precipitation from an ensemble of 16 bias-corrected climate model combinations were blended with a scenario-neutral response surface approach to analyses changes in: (i) the meteorological conditions, (ii) the meteorological water balance, and (iii) selected drought characteristics such as drought intensity, drought months, and drought events, which were derived from the Standardized Precipitation and Evapotranspiration Index. Changes were analyzed for a near-term (2021–2050) and far-term period (2071–2100) with reference to 1976–2005. The effect of bias-correction (i.e., empirical quantile mapping) on the ability of the climate model ensemble to reproduce observed drought characteristics as compared to raw climate projections was also investigated. Results suggest that the bias-correction improves the climate models in terms of reproducing temperature and precipitation statistics but not drought characteristics. Still, despite the differences in the internal structures and uncertainties that exist among the climate models, they all agree on an increase of meteorological droughts in the future in terms of higher drought intensity and longer events. Drought intensity is projected to increase between +25 and +50% during 2021–2050 and between +131 and +388% during 2071–2100. This translates into +3 to +5, and +7 to +8 more drought months per year during both periods, respectively. With longer lasting drought events, the number of drought events decreases. Projected droughts based on the high emission scenario are 1.7 times more severe than droughts based on the moderate scenario. That means that droughts in this region will likely become more severe in the coming decades. Despite the inherent high uncertainties of climate projections, the results provide a basis in planning and (water-)managing activities for climate change adaptation measures in Malawi. This is of particular relevance for water management issues referring hydro power generation and food production, both for rain-fed and irrigated agriculture.
The study examined the potential future changes of drought characteristics in the Greater Lake Malawi Basin in Southeast Africa. This region strongly depends on water resources to generate electricity and food. Future projections (considering both moderate and high emission scenarios) of temperature and precipitation from an ensemble of 16 bias-corrected climate model combinations were blended with a scenario-neutral response surface approach to analyses changes in: (i) the meteorological conditions, (ii) the meteorological water balance, and (iii) selected drought characteristics such as drought intensity, drought months, and drought events, which were derived from the Standardized Precipitation and Evapotranspiration Index. Changes were analyzed for a near-term (2021–2050) and far-term period (2071–2100) with reference to 1976–2005. The effect of bias-correction (i.e., empirical quantile mapping) on the ability of the climate model ensemble to reproduce observed drought characteristics as compared to raw climate projections was also investigated. Results suggest that the bias-correction improves the climate models in terms of reproducing temperature and precipitation statistics but not drought characteristics. Still, despite the differences in the internal structures and uncertainties that exist among the climate models, they all agree on an increase of meteorological droughts in the future in terms of higher drought intensity and longer events. Drought intensity is projected to increase between +25 and +50% during 2021–2050 and between +131 and +388% during 2071–2100. This translates into +3 to +5, and +7 to +8 more drought months per year during both periods, respectively. With longer lasting drought events, the number of drought events decreases. Projected droughts based on the high emission scenario are 1.7 times more severe than droughts based on the moderate scenario. That means that droughts in this region will likely become more severe in the coming decades. Despite the inherent high uncertainties of climate projections, the results provide a basis in planning and (water-)managing activities for climate change adaptation measures in Malawi. This is of particular relevance for water management issues referring hydro power generation and food production, both for rain-fed and irrigated agriculture.
Glaciated high-alpine areas are fundamentally altered by climate change, with well-known implications for hydrology, e.g., due to glacier retreat, longer snow-free periods, and more frequent and intense summer rainstorms. While knowledge on how these hydrological changes will propagate to suspended sediment dynamics is still scarce, it is needed to inform mitigation and adaptation strategies. To understand the processes and source areas most relevant to sediment dynamics, we analyzed discharge and sediment dynamics in high temporal resolution as well as their patterns on several spatial scales, which to date few studies have done.
We used a nested catchment setup in the Upper Ötztal in Tyrol, Austria, where high-resolution (15 min) time series of discharge and suspended sediment concentrations are available for up to 15 years (2006–2020). The catchments of the gauges in Vent, Sölden and Tumpen range from 100 to almost 800 km2 with 10 % to 30 % glacier cover and span an elevation range of 930 to 3772 m a.s.l. We analyzed discharge and suspended sediment yields (SSY), their distribution in space, their seasonality and spatial differences therein, and the relative importance of short-term events. We complemented our analysis by linking the observations to satellite-based snow cover maps, glacier inventories, mass balances and precipitation data.
Our results indicate that the areas above 2500 m a.s.l., characterized by glacier tongues and the most recently deglaciated areas, are crucial for sediment generation in all sub-catchments. This notion is supported by the synchronous spring onset of sediment export at the three gauges, which coincides with snowmelt above 2500 m but lags behind spring discharge onsets. This points at a limitation of suspended sediment supply as long as the areas above 2500 m are snow-covered. The positive correlation of annual SSY with glacier cover (among catchments) and glacier mass balances (within a catchment) further supports the importance of the glacier-dominated areas. The analysis of short-term events showed that summer precipitation events were associated with peak sediment concentrations and yields but on average accounted for only 21 % of the annual SSY in the headwaters. These results indicate that under current conditions, thermally induced sediment export (through snow and glacier melt) is dominant in the study area.
Our results extend the scientific knowledge on current hydro-sedimentological conditions in glaciated high-alpine areas and provide a baseline for studies on projected future changes in hydro-sedimentological system dynamics.
The sensitivity of key hydrologic variables and hydropower generation to climate change in the Lake Malawi and Shire River basins is assessed. The study adapts the mesoscale Hydrological Model (mHM) which is applied separately in the Upper Lake Malawi and Shire River basins. A particular Lake Malawi model, which focuses on reservoir routing and lake water balance, has been developed and is interlinked between the two basins. Climate change projections from 20 Coordinated Regional Climate Downscaling Experiment (CORDEX) models for Africa based on two scenarios (RCP4.5 and RCP8.5) for the periods 2021-2050 and 2071-2100 are used. An annual temperature increase of 1 degrees C decreases mean lake level and outflow by 0.3 m and 17%, respectively, signifying the importance of intensified evaporation for Lake Malawi's water budget. Meanwhile, a +5% (-5%) deviation in annual rainfall changes mean lake level by +0.7 m (-0.6 m). The combined effects of temperature increase and rainfall decrease result in significantly lower flows in the Shire River. The hydrological river regime may change from perennial to seasonal with the combination of annual temperature increase and precipitation decrease beyond 1.5 degrees C (3.5 degrees C) and -20% (-15%). The study further projects a reduction in annual hydropower production between 1% (RCP8.5) and 2.5% (RCP4.5) during 2021-2050 and between 5% (RCP4.5) and 24% (RCP8.5) during 2071-2100. The results show that it is of great importance that a further development of hydro energy on the Shire River should take into account the effects of climate change, e.g., longer low flow periods and/or higher discharge fluctuations, and thus uncertainty in the amount of electricity produced.
Stofftransport in einem Lösseinzugsgebiet: Experimentelle Evidenz und numerische Modellierung.
(2004)
Detention areas provide a means to lower peak discharges in rivers by temporarily storing excess water. In the case of extreme flood events, the storage effect reduces the risk of dike failures or extensive inundations for downstream reaches and near the site of abstraction. Due to the large amount of organic matter contained in the river water and the inundation of terrestrial vegetation in the detention area, a deterioration of water quality may occur. In particular, decay processes can cause a severe depletion of dissolved oxygen (DO) in the temporary water body. In this paper, we studied the potential of a water quality model to simulate the DO dynamics in a large but shallow detention area to be built at the Elbe River (Germany). Our focus was on examining the impact of spatial discretization on the model's performance and usability. Therefore, we used a zero-dimensional (OD) and a two-dimensional (2D) modeling approach in parallel. The two approaches solely differ in their spatial discretization, while conversion processes, parameters, and boundary conditions were kept identical. The dynamics of DO simulated by the two models are similar in the initial flooding period but diverge when the system starts to drain. The deviation can be attributed to the different spatial discretization of the two models, leading to different estimates of flow velocities and water depths. Only the 2D model can account for the impact of spatial variability on the evolution of state variables. However, its application requires high efforts for pre- and post-processing and significantly longer computation times. The 2D model is, therefore, not suitable for investigating various flood scenarios or for analyzing the impact of parameter uncertainty. For practical applications, we recommend to firstly set up a fast-running model of reduced spatial discretization, e.g. a OD model. Using this tool, the reliability of the simulation results should be checked by analyzing the parameter uncertainty of the water quality model. A particular focus may be on those parameters that are spatially variable and, therefore, believed to be better represented in a 2D approach. The benefit from the application of the more costly 2D model should be assessed, based on the analyses carried out with the OD model. A 2D model appears to be preferable only if the simulated detention area has a complex topography, flow velocities are highly variable in space, and the parameters of the water quality model are well known.
Simple water balance modelling of surface reservoir systems in a large data-scarce semiarid region
(2004)
Water resources in dryland areas are often provided by numerous surface reservoirs. As a basis for securing future water supply, the dynamics of reservoir systems need to be simulated for large river basins, accounting for environmental change and an increasing water demand. For the State of Ceara in semiarid Northeast Brazil, with several thousands of reservoirs, a simple deterministic water balance model is presented. Within a cascade-type approach, the reservoirs are grouped into six classes according to storage capacity, rules for flow routing between reservoirs of different size are defined, and water withdrawal and return flow due to human water use is accounted for. While large uncertainties in model applications exist, particularly in terms of reservoir operation rules, model validation against observed reservoir storage volumes shows that the approach is a reasonable simplification to assess surface water availability in large river basins. The results demonstrate the large impact of reservoir storage on downstream flow and stress the need for a coupled simulation of runoff generation, network redistribution and water use
The spatial variability of landscape features such as topography, soils and vegetation defines the spatial pattern of hydrological state variables like soil moisture. Spatial variability thereby controls the functional behaviour of the landscape in terms of its runoff response. A consequence of spatial variability is that exchange processes between landscape patches can occur at various spatial scales ranging from the plot to the basin scale. In semi-arid areas, the lateral redistribution of surface runoff between adjacent landscape patches is an important process. For applications to large river basins of 10(4)-10(5) km(2) in size, a multi-scale landscape discretization scheme is presented in this paper. The landscape is sub-divided into modelling units within a hierarchy of spatial scale levels. By delineating areas characterized by a typical toposequence, organised and random variability of landscape characteristics is captured in the model. Using runoff-runon relationships with transition frequencies based on areal fractions of modelling units, lateral surface and subsurface water fluxes between modelling units at the hillslope scale are represented. Thus, the new approach allows for a manageable description of interactions between fine-scale landscape features for inclusion in coarse-scale models. Model applications for the State of Ceara (148,000 km(2)) in the north- east of Brazil demonstrate the importance of taking into account landscape variability and interactions between landscape patches in a semi-arid environment. Using mean landscape characteristics leads to a considerable underestimation of infiltration-excess surface runoff and total simulated runoff. Re-infiltration of surface runoff and lateral redistribution processes between landscape patches cause a reduction of runoff volumes at the basin scale and contribute to the amplification of variations in runoff volumes relative to variations in rainfall volumes for semi-arid areas. (C) 2004 Elsevier B.V. All rights reserved
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.
The intention of the presented study is to gain a better understanding of the mechanisms that caused the bimodal rainfall-runoff responses which occurred up to the mid-1970s regularly in the Schafertal catchment and vanished after the onset of mining activities. Understanding, this process is a first step to understanding the ongoing hydrological change in this area. It is hypothesized that either subsurface stormflow, or fast displacement of groundwater, could cause the second delayed peak. A top-down analysis of rainfall-runoff data, field observations as well as process modelling are combined within a rejectionistic framework. A statistical analysis is used to test whether different predictors. which characterize the forcing. near surface water content and deeper subsurface store, allow the prediction of the type of rainfall-runoff response. Regression analysis is used with generalized linear models Lis they can deal with non-Gaussian error distributions Lis well its a non-stationary variance. The analysis reveals that the dominant predictors are the pre-event discharge (proxy of state of the groundwater store) and the precipitation amount, In the field campaign, the subsurface at a representative hillslope was investigated by means of electrical resistivity tomography in order to identify possible strata as flow paths for subsurface stormflow. A low resistivity in approximately 4 in depth-either due to a less permeable layer or the groundwater surface-was detected. The former Could serve as a flow path for subsurface stormflow. Finally, the physical-based hydrological model CATFLOW and the groundwater model FEFLOW are compared with respect to their ability to reproduce the bimodal runoff responses. The groundwater model is able to reproduce the observations, although it uses only an abstract representation of the hillslopes. Process model analysis as well Lis statistical analysis strongly suggest that fast displacement of groundwater is the dominant process underlying the bimodal runoff reactions.
Probleme, Grenzen und Herausforderungen der hydrologischen Modellierung: Wasserhaushalt und Abfluss
(2004)
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 strong correlation between average soil moisture dynamics and runoff coefficients of rainfall-runoff events observed at gauge Rehefeld, which explains almost as much variability in the runoff coefficients as pre-event discharge. By combining these results with a recession analysis we derived a first conceptual model of the dominant runoff mechanisms operating in this catchment. Finally, long term simulations with a physically based hydrological model were in good/acceptable accordance with the time series of spatial average soil water content observed at the forested site and the grassland site, respectively. Both simulations used a homogeneous soil setup that closely reproduces observed average soil conditions observed at the field sites. This corroborates the proposed sampling strategy of clustering TDR probes in typical functional units is a promising technique to explore the soil moisture control on runoff generation. Long term monitoring of such sites could maybe yield valuable information for flood warning. The sampling strategy helps furthermore to unravel different types of soil moisture variability.
Modelling the effects of climate change on water availability in the semi-arid of North-East Brazil
(2001)
A methodology is presented to assess the impact of reservoir silting oil water availability for semiarid environments, applied to seven representative watersheds in the state of Ceara, Brazil. Water yield is computed using stochastic modelling for several reliability levels and water yield reduction is quantified for the focus areas. The yield-volume elasticity concept, which indicates the relative yield reduction in terms of relative storage capacity of the reservoirs, is presented and applied. Results chow that storage capacity was reduced by 0.2% year(-1) due to silting, that the risk of water shortage almost doubled in less than 50 years for the most critical reservoir, and that reduction of storage capacity had three times more impact oil yield reduction than the increase in evaporation. Average 90% reliable yield-volume elasticity was 0.8, which means that the global water yield (Q(90)) in Ceara is expected to diminish yearly by 388 L s(-1) due to reservoir silting
Recent climatic changes have the potential to severely alter river runoff, particularly in snow-dominated river basins. Effects of changing snow covers superimpose with changes in precipitation and anthropogenic modifications of the watershed and river network. In the attempt to identify and disentangle long-term effects of different mechanisms, we employ a set of analytical tools to extract long-term changes in river runoff at high resolution. We combine quantile sampling with moving average trend statistics and empirical mode decomposition and apply these tools to discharge data recorded along rivers with nival, pluvial and mixed flow regimes as well as temperature and precipitation data covering the time frame 1869-2016. With a focus on central Europe, we analyse the long-term impact of snow cover and precipitation changes along with their interaction with reservoir constructions.
Our results show that runoff seasonality of snow-dominated rivers decreases. Runoff increases in winter and spring, while discharge decreases in summer and at the beginning of autumn. We attribute this redistribution of annual flow mainly to reservoir constructions in the Alpine ridge. During the course of the last century, large fractions of the Alpine rivers were dammed to produce hydropower. In recent decades, runoff changes induced by reservoir constructions seem to overlap with changes in snow cover. We suggest that Alpine signals propagate downstream and affect runoff far outside the Alpine area in river segments with mixed flow regimes. Furthermore, our results hint at more (intense) rain-fall in recent decades. Detected increases in high discharge can be traced back to corresponding changes in precipitation.
Recent climatic changes have the potential to severely alter river runoff, particularly in snow-dominated river basins. Effects of changing snow covers superimpose with changes in precipitation and anthropogenic modifications of the watershed and river network. In the attempt to identify and disentangle long-term effects of different mechanisms, we employ a set of analytical tools to extract long-term changes in river runoff at high resolution. We combine quantile sampling with moving average trend statistics and empirical mode decomposition and apply these tools to discharge data recorded along rivers with nival, pluvial and mixed flow regimes as well as temperature and precipitation data covering the time frame 1869-2016. With a focus on central Europe, we analyse the long-term impact of snow cover and precipitation changes along with their interaction with reservoir constructions.
Our results show that runoff seasonality of snow-dominated rivers decreases. Runoff increases in winter and spring, while discharge decreases in summer and at the beginning of autumn. We attribute this redistribution of annual flow mainly to reservoir constructions in the Alpine ridge. During the course of the last century, large fractions of the Alpine rivers were dammed to produce hydropower. In recent decades, runoff changes induced by reservoir constructions seem to overlap with changes in snow cover. We suggest that Alpine signals propagate downstream and affect runoff far outside the Alpine area in river segments with mixed flow regimes. Furthermore, our results hint at more (intense) rain-fall in recent decades. Detected increases in high discharge can be traced back to corresponding changes in precipitation.
Landnutzung und Hochwasserentstehung : Modellierung anhand dreier mesoskaliger Einzugsgebiete
(2002)
Stand des IMAF zu Beginn des Jahres 2006
Zum 1. April 2005 wurde per Beschluss des Rektorats der Universität Potsdam das Interdisziplinäre Zentrum für Musterdynamik und Angewandte Fernerkundung (IMAF) an der Universität Potsdam eingerichtet. Diesem Beschluss gingen knapp zwei Jahre konzeptionelle, organisatorische und administrative Vorarbeiten voraus. Inzwischen ist das IMAF also offiziell gegründet, der Vorstand wurde „bestellt“ (Prof. M. Mutti. Prof. E. Zehe, Prof. A. Bronstert), der Geschäftsführer bzw. wissenschaftliche Koordinator Dr. M. Kühling arbeitet in dieser Funktion seit Sommer 2005 und seit kurzem ist auch die 1. Version der Homepage des IMAF (http://www.uni-potsdam.de/imaf/) frei geschaltet. Auch die Infrastruktur des IMAF ist in der Entstehungsphase: Büroräume sind versprochen (wenn auch noch nicht bezugsfertig) im Haus 13 auf dem Campus Golm der Universität Potsdam und der 1. erfolgreiche Drittmittelantrag erbrachte 8 leistungsfähige Tischrechner und einen Server für das IMAF aus EU-Mitteln. Wichtiger als die administrativen und organisatorischen Arbeiten sind aber die inhaltlichen Forstschritte. Hier ist die große Resonanz, die die Gründung des IMAF sowohl innerhalb als auch außerhalb der Universität gefunden hat, besonders erfreulich. Über 30 Angehörige des Zentrums sind inzwischen zu verzeichnen und es gibt bereits eine Reihe von wissenschaftlichen Projektinitiativen und Ideen für dieses Zentrum. Neben den wissenschaftlichen Arbeiten am IMAF ist ein zweites Hauptziel für dieses Zentrum die Entwicklung und der Ausbau eines strukturierten Ausbildungsangebotes für Musterdynamik und angewandte Fernerkundung. Dies sollen gleichermaßen Masterstudenten als auch Doktoranden der Universität Potsdam und der mit ihr assoziierten außeruniversitären Institute nutzen. Zudem werden Kurse und Weiterbildungsveranstaltungen mit nationalen und internationalen Experten angestrebt. Neben diesen positiven Entwicklungen gibt es auch (noch ??) über einige Mängel zu berichten:
Das Sekretariat ist nach wie vor unbesetzt, die Finanzausstattung des Zentrums ist völlig ungenügend und die im Konzept für das Zentrum beantragte Wissenschaftlerstelle für Softwareanwendung ist nicht in Sicht. Für einen Erfolg des Zentrums ist es unbedingt notwendig, dass sich diese Situation deutlich verbessert!!
Forschungsschwerpunkte des IMAF
Räumliche Muster und deren Struktur in der Umwelt
Räumliche Muster sind in vielen naturwissenschaftlichen Disziplinen (Hydrologie, Ökologie, Geologie, Biologie, Chemie, Physik) von zentraler Bedeutung. Z.B. bestimmen die räumlichen (und zeitlichen) Muster von Bodeneigenschaften und Vegetation in ihrem Zusammenspiel mit den Mustern von Niederschlag und Strahlungsinput maßgeblich den Wasser- und Stoffhaushalt auf unterschiedlichsten Skalen und führen über Rückkopplung wiederum zu Veränderungen in Klima, Vegetation und Ökosystemen. Vom kleinräumigen Transport von Schadstoffen und von der Hochwasserentstehung bis zur Frage nach den regionalen und globalen Veränderungen von Klima, Vegetation und Landnutzung seien hier nur einige Problemkreise genannt, in denen Muster und Musterdynamik eine zentrale Stellung einnehmen. Darüber hinaus liefert die Betrachtung der zeitlichen Veränderung von räumlichen Mustern, in Ergänzung zur klassischen Erfassung dynamischer Prozesse in Form von Messungen lokaler zeitlicher Änderungen, eine völlig neue Perspektive auf Dynamik und eröffnet damit völlig neue wissenschaftliche Möglichkeiten. Aktuelle und sehr drängende Fragen innerhalb dieses Forschungsschwerpunktes sind unter anderem:
• Analyse der generelle Raumstruktur von Geodaten (Variabilität, Struktur, Konnektivität);
• Thematische Verbindungen verschiedener Datenebenen und Möglichkeiten für deren Assimilation;
• Möglichkeiten und Grenzen des Skalenübergangs zwischen verschiedenen räumlichen Auflösungen und Informationsquellen;
• Ableitung der zeitlichen Dynamik bzw. Entwicklung von großen flächenhaften Datenfeldern.
Angewandte Fernerkundung
Wie keine andere Technik bietet die Fernerkundung in jeglicher Form (unter anderem Satelliten, flugzeuggetragene Sensoren, Wetterradar und auch geophysikalische Methoden) umfangreiche Möglichkeiten, räumliche Muster und deren zeitliche Veränderungen zu erfassen. Allen Methoden der Fernerkundung gemein ist, dass sie nur indirekte Ergebnisse liefern. Das heißt, es besteht nur ein mittelbarer Zusammenhang zwischen dem beobachteten Signal, meist der Reflektivität oder Emissivität elektromagnetischer Strahlung in verschiedenen Spektralbereichen (optisch oder Radar), und der eigentlich interessierenden Größe, wie dem Feuchtezustand der Vegetation, der Bodenfeuchte oder Bodenrauhigkeit, der Niederschlagsintensität, dem Zustand der Schneedecke oder der Ausdehnung eines Oberflächenfilms auf Gewässern. Ein Satellitenbild enthält beispielsweise immer die spektrale Signatur des räumlichen Musters mehrerer der oben genannten Einflussgrößen, was die Extraktion oder Diskriminierung der eigentlich interessierenden Größe erschwert. Dieser „vermischte“ Charakter der Fernerkundungsdaten bietet aber auch immense Chancen. So lassen sich durch geeignete Interpretationsverfahren aus jedem mit hohem finanziellem und technischem Aufwand erstellten Satellitenbild zahlreiche und im Detail völlig unterschiedliche Fragestellungen bearbeiten. Die Extraktion der gewünschten Information aus dem Fernerkundungssignal führt mathematisch gesehen meist auf die Lösung so genannter inverser, schlecht gestellter Probleme. Somit beinhaltet die interdisziplinäre Nutzung von Fernerkundung auch ein hohes methodisches Synergiepotential. Durch die heutigen technischen Möglichkeiten zur Archivierung auch sehr umfangreicher raumbezogener Informationen ist die Bearbeitung zu jedem beliebigen Zeitpunkt nach der Aufnahme möglich – zum Beispiel bis entsprechend lange Zeitreihen und/oder geeignete Interpretationsverfahren zur Verfügung stehen. Tatsächlich dürfte der weitaus größte Teil der raumbezogenen Informationen, die in den bisher erhobenen Fernerkundungsdaten stecken, nur in Ansätzen ausgewertet sein. Einer bereits sehr hoch entwickelten technischen Dimension der Fernerkundung steht ein gewisses Defizit im Umfang ihrer Anwendung in den verschiedenen naturwissenschaftlichen Disziplinen gegenüber. Aktuelle und sehr drängende Fragen innerhalb dieses Forschungsschwerpunktes sind unter anderem:
• Nutzung der räumlichen und inhaltlichen Breite von Fernerkundungsinformationen;
• Verbindung mit automatisierten, u.a. geophysikalischen Methoden des „ground-truthings“;
• Identifizierung der Grenzen bzgl. Repräsentanz der Daten (spektral, raum-zeitliche Auflösung);
• Verbindung unterschiedlicher Methoden der Fernerkundung und der Geophysik.
Dieser Beitrag illustriert die o.g. Fragestellungen anhand einiger Darstellungen aus verschiedenen wissenschaftlichen Disziplinen und erläutert 2 Beispiele zu beabsichtigten Forschungsprojekten:
• Erfassung und Bedeutung von Boden-Oberflächeneigenschaften auf die Abflussbildung von Landschaften;
• Phänomene des Stofftransportes in homogenen vs. heterogenen Böden.
The co-occurrence of warm spells and droughts can lead to detrimental socio-economic and ecological impacts, largely surpassing the impacts of either warm spells or droughts alone. We quantify changes in the number of compound warm spells and droughts from 1979 to 2018 in the Mediterranean Basin using the ERA5 data set. We analyse two types of compound events: 1) warm season compound events, which are extreme in absolute terms in the warm season from May to October and 2) year-round deseasonalised compound events, which are extreme in relative terms respective to the time of the year. The number of compound events increases significantly and especially warm spells are increasing strongly – with an annual growth rates of 3.9 (3.5) % for warm season (deseasonalised) compound events and 4.6 (4.4) % for warm spells –, whereas for droughts the change is more ambiguous depending on the applied definition. Therefore, the rise in the number of compound events is primarily driven by temperature changes and not the lack of precipitation. The months July and August show the highest increases in warm season compound events, whereas the highest increases of deseasonalised compound events occur in spring and early summer. This increase in deseasonalised compound events can potentially have a significant impact on the functioning of Mediterranean ecosystems as this is the peak phase of ecosystem productivity and a vital phenophase.
The co-occurrence of warm spells and droughts can lead to detrimental socio-economic and ecological impacts, largely surpassing the impacts of either warm spells or droughts alone. We quantify changes in the number of compound warm spells and droughts from 1979 to 2018 in the Mediterranean Basin using the ERA5 data set. We analyse two types of compound events: 1) warm season compound events, which are extreme in absolute terms in the warm season from May to October and 2) year-round deseasonalised compound events, which are extreme in relative terms respective to the time of the year. The number of compound events increases significantly and especially warm spells are increasing strongly – with an annual growth rates of 3.9 (3.5) % for warm season (deseasonalised) compound events and 4.6 (4.4) % for warm spells –, whereas for droughts the change is more ambiguous depending on the applied definition. Therefore, the rise in the number of compound events is primarily driven by temperature changes and not the lack of precipitation. The months July and August show the highest increases in warm season compound events, whereas the highest increases of deseasonalised compound events occur in spring and early summer. This increase in deseasonalised compound events can potentially have a significant impact on the functioning of Mediterranean ecosystems as this is the peak phase of ecosystem productivity and a vital phenophase.
Hydro Explorer
(2021)
Climatic changes and anthropogenic modifications of the river basin or river network have the potential to fundamentally alter river runoff. In the framework of this study, we aim to analyze and present historic changes in runoff timing and runoff seasonality observed at river gauges all over the world. In this regard, we develop the Hydro Explorer, an interactive web app, which enables the investigation of >7,000 daily resolution discharge time series from the Global Runoff Data Centre (GRDC). The interactive nature of the developed web app allows for a quick comparison of gauges, regions, methods, and time frames. We illustrate the available analytical tools by investigating changes in runoff timing and runoff seasonality in the Rhine River Basin. Since we provide the source code of the application, existing analytical approaches can be modified, new methods added, and the tool framework can be re-used to visualize other data sets.
In recent years, urban and rural flash floods in Europe and abroad have gained considerable attention because of their sudden occurrence, severe material damages and even danger to life of inhabitants. This contribution addresses questions about possibly changing environmental conditions which might have altered the occurrence frequencies of such events and their consequences. We analyze the following major fields of environmental changes.
Altered high intensity rain storm conditions, as a consequence of regionalwarming; Possibly altered runoff generation conditions in response to high intensity rainfall events; Possibly altered runoff concentration conditions in response to the usage and management of the landscape, such as agricultural, forest practices or rural roads; Effects of engineering measures in the catchment, such as retention basins, check dams, culverts, or river and geomorphological engineering measures.
We take the flash-flood in Braunsbach, SW-Germany, as an example, where a particularly concise flash flood event occurred at the end of May 2016. This extreme cascading natural event led to immense damage in this particular village. The event is retrospectively analyzed with regard to meteorology, hydrology, geomorphology and damage to obtain a quantitative assessment of the processes and their development.
The results show that it was a very rare rainfall event with extreme intensities, which in combination with catchment properties and altered environmental conditions led to extreme runoff, extreme debris flow and immense damages. Due to the complex and interacting processes, no single flood cause can be identified, since only the interplay of those led to such an event. We have shown that environmental changes are important, but-at least for this case study-even natural weather and hydrologic conditions would still have resulted in an extreme flash flood event.
We generated medium-range forecasts of runoff for a 50 km(2) headwater catchment upstream of a reservoir using numerical weather predictions (NWPs) of the past as input to an operational hydrological model. NWP data originating from different sources were tested. For a period of 8.5 years, we computed daily forecasts with a lead time of +120 h based on an empirically downscaled version of the ECMWF's ensemble prediction system. For the last 3.5 years of the test period, we also tried the deterministic COSMO-EU forecast disseminated by the German Weather Service for lead times of up to +72 h. Common measures of skill indicate superiority of the ensemble runoff forecast over single-value forecasts for longer lead times. However, regardless of which NWP data were being used, the probability of event detection (POD) was found to be generally lower than 50%. In many cases, values in the range of 20-30% were obtained. At the same time, the false alarms ratio (FAR) was often found to be considerably high. The observed uncertainties in the hydrological forecasts were shown to originate from both the insufficient quality of precipitation forecasts as well as deficiencies in hydrological modeling and quantitative precipitation estimation. With respect to the anticipatory control of reservoirs in the studied catchment, the value of the tested runoff forecasts appears to be limited. This is due to the unfavorably low POD/FAR ratio in conjunction with a high cost-loss ratio. However, our results indicate that, in many cases, major runoff events related to snow melt can be successfully predicted as early as 4-5 days in advance.
Due to the environmental and socio-economic importance of mountainous regions, it is crucial to understand causes and consequences of climatic changes in those sensitive landscapes. Daily resolution alpine climate data from Switzerland covering an elevation range of over 3,000m between 1981 and 2017 have been analysed using highly resolved trends in order to gain a better understanding of features, forcings and feedbacks related to temperature changes in mountainous regions. Particular focus is put on processes related to changes in weather types, incoming solar radiation, cloud cover, air humidity, snow/ice and elevation dependency of temperature trends. Temperature trends in Switzerland differ depending on the time of the year, day and elevation. Warming is strongest during spring and early summer with enhanced warming of daytime maximum temperatures. Elevation-based differences in temperature trends occur during autumn and winter with stronger warming at lower elevations. We attribute this elevation-dependent temperature signal mainly to elevation-based differences in trends of incoming solar radiation and elevation-sensitive responses to changes in frequencies of weather types. In general, effects of varying frequencies of weather types overlap with trends caused by transmission changes in short- and long-wave radiation. Temperature signals arising from snow/ice albedo feedback mechanisms are probably small and might be hidden by other effects.
Elevation-dependent compensation effects in snowmelt in the Rhine River Basin upstream gauge Basel
(2021)
In snow-dominated river basins, floods often occur during early summer, when snowmelt-induced runoff superimposes with rainfall-induced runoff. An earlier onset of seasonal snowmelt as a consequence of a warming climate is often expected to shift snowmelt contribution to river runoff and potential flooding to an earlier date. Against this background, we assess the impact of rising temperatures on seasonal snowpacks and quantify changes in timing, magnitude and elevation of snowmelt. We analyse in situ snow measurements, conduct snow simulations and examine changes in river runoff at key gauging stations. With regard to snowmelt, we detect a threefold effect of rising temperatures: snowmelt becomes weaker, occurs earlier and forms at higher elevations. Due to the wide range of elevations in the catchment, snowmelt does not occur simultaneously at all elevations. Results indicate that elevation bands melt together in blocks. We hypothesise that in a warmer world with similar sequences of weather conditions, snowmelt is moved upward to higher elevation. The movement upward the elevation range makes snowmelt in individual elevation bands occur earlier, although the timing of the snowmelt-induced runoff stays the same. Meltwater from higher elevations, at least partly, replaces meltwater from elevations below.
The presence of impermeable surfaces in urban areas hinders natural drainage and directs the surface runoff to storm drainage systems with finite capacity, which makes these areas prone to pluvial flooding. The occurrence of pluvial flooding depends on the existence of minimal areas for surface runoff generation and concentration. Detailed hydrologic and hydrodynamic simulations are computationally expensive and require intensive resources. This study compared and evaluated the performance of two simplified methods to identify urban pluvial flood-prone areas, namely the fill–spill–merge (FSM) method and the topographic wetness index (TWI) method and used the TELEMAC-2D hydrodynamic numerical model for benchmarking and validation. The FSM method uses common GIS operations to identify flood-prone depressions from a high-resolution digital elevation model (DEM). The TWI method employs the maximum likelihood method (MLE) to probabilistically calibrate a TWI threshold (τ) based on the inundation maps from a 2D hydrodynamic model for a given spatial window (W) within the urban area. We found that the FSM method clearly outperforms the TWI method both conceptually and effectively in terms of model performance.
The presence of impermeable surfaces in urban areas hinders natural drainage and directs the surface runoff to storm drainage systems with finite capacity, which makes these areas prone to pluvial flooding. The occurrence of pluvial flooding depends on the existence of minimal areas for surface runoff generation and concentration. Detailed hydrologic and hydrodynamic simulations are computationally expensive and require intensive resources. This study compared and evaluated the performance of two simplified methods to identify urban pluvial flood-prone areas, namely the fill–spill–merge (FSM) method and the topographic wetness index (TWI) method and used the TELEMAC-2D hydrodynamic numerical model for benchmarking and validation. The FSM method uses common GIS operations to identify flood-prone depressions from a high-resolution digital elevation model (DEM). The TWI method employs the maximum likelihood method (MLE) to probabilistically calibrate a TWI threshold (τ) based on the inundation maps from a 2D hydrodynamic model for a given spatial window (W) within the urban area. We found that the FSM method clearly outperforms the TWI method both conceptually and effectively in terms of model performance.
As a consequence of increasing winter rainfall totals and intensities over the second half of the 20th century, signs of increased flooding probability in many areas of the Rhine and Meuse basins have been documented. These changes affecting rainfall characteristics are most evidently due to an increase in westerly atmospheric circulation types. Land use changes, particularly urbanization, can have significant local effects in small basins (headwaters) with respect to flooding, especially during heavy local rainstorms, but no evidence exists that land use change has had significant effects on peak flows in the rivers Rhine and Meuse. For the 21st century, most global circulation models suggest higher winter rainfall totals. Most hydrological simulations of the Rhine-Meuse river basins suggest an increased flooding probability, with a progressive shift of the Rhine from a 'rain-fed/meltwater' river into a mainly 'rain-fed' river. A very limited effect of changes in land use on the discharge regime seems to exist for the main branches of the Meuse and Rhine rivers. For mesoscale basins, future changes in peak flows depend on the changes in the variability of extreme precipitations in combination with land use changes. Copyright (C) 2004 John Wiley Sons, Ltd
Study region: Tisza and Prut catchments, originating on the slopes of the Carpathian mountains. Study focus: The study reported here investigates (i) climate change impacts on flood risk in the region, and (ii) uncertainty related to hydrological modelling, downscaling techniques and climate projections. The climate projections used in the study were derived from five GCMs, downscaled either dynamically with RCMs or with the statistical downscaling model XDS. The resulting climate change scenarios were applied to drive the eco-hydrological model SWIM, which was calibrated and validated for the catchments in advance using observed climate and hydrological data. The changes in the 30-year flood hazards and 98 and 95 percentiles of discharge were evaluated for the far future period (2071-2100) in comparison with the reference period (1981-2010). New hydrological insights for the region: The majority of model outputs under RCP 4.5 show a small to strong increase of the 30-year flood level in the Tisza ranging from 4.5% to 62%, and moderate increase in the Prut ranging from 11% to 22%. The impact results under RCP 8.5 are more uncertain with changes in both directions due to high uncertainties in GCM-RCM climate projections, downscaling methods and the low density of available climate stations.
Floods and debris flows in small Alpine torrent catchments (<10km(2)) arise from a combination of critical antecedent system state conditions and mostly convective precipitation events with high precipitation intensities. Thus, climate change may influence the magnitude-frequency relationship of extreme events twofold: by a modification of the occurrence probabilities of critical hydrological system conditions and by a change of event precipitation characteristics. Three small Alpine catchments in different altitudes in Western Austria (Ruggbach, Brixenbach and Langentalbach catchment) were investigated by both field experiments and process-based simulation. Rainfall-runoff model (HQsim) runs driven by localized climate scenarios (CNRM-RM4.5/ARPEGE, MPI-REMO/ECHAM5 and ICTP-RegCM3/ECHAM5) were used in order to estimate future frequencies of stormflow triggering system state conditions. According to the differing altitudes of the study catchments, two effects of climate change on the hydrological systems can be observed. On one hand, the seasonal system state conditions of medium altitude catchments are most strongly affected by air temperature-controlled processes such as the development of the winter snow cover as well as evapotranspiration. On the other hand, the unglaciated high-altitude catchment is less sensitive to climate change-induced shifts regarding days with critical antecedent soil moisture and desiccated litter layer due to its elevation-related small proportion of sensitive areas. For the period 2071-2100, the number of days with critical antecedent soil moisture content will be significantly reduced to about 60% or even less in summer in all catchments. In contrast, the number of days with dried-out litter layers causing hydrophobic effects will increase by up to 8%-11% of the days in the two lower altitude catchments. The intensity analyses of heavy precipitation events indicate a clear increase in rain intensities of up to 10%.
Entlang der Küstenniederung des Naturschutzgebietes „Hütelmoor und Heiligensee“, ca. 6 km nordöstlich von Rostock-Warnemünde gelegen, wird seit dem Jahr 2000 die Küstendüne nicht mehr instand gehalten. Im Rahmen der Renaturierung des Gebietes werden so grundsätzlich wieder Überflutungen bei Ostseehochwassern zugelassen, was bisher jedoch noch nicht eingetreten ist. Am 4./5. Januar 2017 ereignete sich ein Sturmhochwasser der Ostsee, mit einem Scheitelwasserstand in Warnemünde, der sich zwischen dem 10- und 20-jährlichen Hochwasserstand einordnet. Dennoch kam es bei diesem Ereignis nicht zum Dünendurchbruch und zur seeseitigen Überflutung, wohl aber zum binnenseitigen Einstrom von Salz- bzw. Brackwasser. Dieser erfolgte über den Graben, durch den das Gebiet normalerweise über die Warnow in die Ostsee entwässert. Durch das Einströmen über die Sohlschwelle, sonst Auslass des Gebietes, stiegen die Wasserstände und Salzkonzentrationen in der südwestlichen Hälfte der Niederung an. Mit zunehmender Entfernung zur Sohlschwelle waren diese Auswirkungen jedoch geringer spürbar. Dies gilt wegen der Retentionswirkung der Niederung mehr für den Wasserstand als für die Salzkonzentration. Während der Wasserstand durch den Einstau der Niederung und Überschwemmungen flächenhaft anstieg, breitete sich die Salzfront präferentiell in den ehemaligen Entwässerungsgräben, die trotz des Einstaus nach wie vor hydraulisch aktiv sind, eher linienhaft aus. Diese Interpretation beruht auf Messergebnissen von Wasserstand, elektrischer Leitfähigkeit und Wassertemperatur.
An ensemble of 10 hydrological models was applied to the same set of land use change scenarios. There was general agreement about the direction of changes in the mean annual discharge and 90% discharge percentile predicted by the ensemble members, although a considerable range in the magnitude of predictions for the scenarios and catchments under consideration was obvious. Differences in the magnitude of the increase were attributed to the different mean annual actual evapotranspiration rates for each land use type. The ensemble of model runs was further analyzed with deterministic and probabilistic ensemble methods. The deterministic ensemble method based on a trimmed mean resulted in a single somewhat more reliable scenario prediction. The probabilistic reliability ensemble averaging (REA) method allowed a quantification of the model structure uncertainty in the scenario predictions. It was concluded that the use of a model ensemble has greatly increased our confidence in the reliability of the model predictions.
Approximation of Groundwater - Surface Water - Interactions in a Mesoscale Lowland River Catchment
(2004)
Advances in Flood Research
(2002)
Channel transmission losses in drylands take place normally in extensive alluvial channels or streambeds underlain by fractured rocks. They can play an important role in streamflow rates, groundwater recharge, freshwater supply and channel-associated ecosystems. We aim to develop a process-oriented, semi-distributed channel transmission losses model, using process formulations which are suitable for data-scarce dryland environments and applicable to both hydraulically disconnected losing streams and hydraulically connected losing(/gaining) streams. This approach should be able to cover a large variation in climate and hydro-geologic controls, which are typically found in dryland regions of the Earth. Our model was first evaluated for a losing/gaining, hydraulically connected 30 km reach of the Middle Jaguaribe River (MJR), Ceara, Brazil, which drains a catchment area of 20 000 km(2). Secondly, we applied it to a small losing, hydraulically disconnected 1.5 km channel reach in the Walnut Gulch Experimental Watershed (WGEW), Arizona, USA. The model was able to predict reliably the streamflow volume and peak for both case studies without using any parameter calibration procedure. We have shown that the evaluation of the hypotheses on the dominant hydrological processes was fundamental for reducing structural model uncertainties and improving the streamflow prediction. For instance, in the case of the large river reach (MJR), it was shown that both lateral stream-aquifer water fluxes and groundwater flow in the underlying alluvium parallel to the river course are necessary to predict streamflow volume and channel transmission losses, the former process being more relevant than the latter. Regarding model uncertainty, it was shown that the approaches, which were applied for the unsaturated zone processes (highly nonlinear with elaborate numerical solutions), are much more sensitive to parameter variability than those approaches which were used for the saturated zone (mathematically simple water budgeting in aquifer columns, including backwater effects). In case of the MJR-application, we have seen that structural uncertainties due to the limited knowledge of the subsurface saturated system interactions (i.e. groundwater coupling with channel water; possible groundwater flow parallel to the river) were more relevant than those related to the subsurface parameter variability. In case of the WEGW application we have seen that the non-linearity involved in the unsaturated flow processes in disconnected dryland river systems (controlled by the unsaturated zone) generally contain far more model uncertainties than do connected systems controlled by the saturated flow. Therefore, the degree of aridity of a dryland river may be an indicator of potential model uncertainty and subsequent attainable predictability of the system.