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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.
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.
Meteorological and hydrological drought assessment in Lake Malawi and Shire River basins (1970-2013)
(2020)
The study assesses the variability and trends of both meteorological and hydrological droughts from 1970 to 2013 in Lake Malawi and Shire River basins using the standardized precipitation index (SPI) and standardized precipitation and evaporation index (SPEI) for meteorological droughts and the lake level change index (LLCI) for hydrological droughts. Trends and slopes in droughts and drought drivers are estimated using Mann-Kendall test and Sen's slope, respectively. Results suggest that meteorological droughts are increasing due to a decrease in precipitation which is exacerbated by an increase in temperature (potential evapotranspiration). The hydrological system of Lake Malawi seems to have a >24-month memory towards meteorological conditions, since the 36-month SPEI can predict hydrological droughts 10 months in advance. The study has found the critical lake level that would trigger hydrological drought to be 474.1 m a.s.l. The increase in drought is a concern as this will have serious impacts on water resources and hydropower supply in Malawi.
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.
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 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.
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.
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.
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.
Ecohydrology analyses the interactions of biotic and abiotic aspects of our ecosystems and landscapes. It is a highly diverse discipline in terms of its thematic and methodical research foci. This article gives an overview of current German ecohydrological research approaches within plant-animal-soil-systems, meso-scale catchments and their river networks, lake systems, coastal areas and tidal rivers. It discusses their relevant spatial and temporal process scales and different types of interactions and feedback dynamics between hydrological and biotic processes and patterns. The following topics are considered key challenges: innovative analysis of the interdisciplinary scale continuum, development of dynamically coupled model systems, integrated monitoring of coupled processes at the interface and transition from basic to applied ecohydrological science to develop sustainable water and land resource management strategies under regional and global change.
The characteristics of a landscape pose essential factors for hydrological processes. Therefore, an adequate representation of the landscape of a catchment in hydrological models is vital. However, many of such models exist differing, amongst others, in spatial concept and discretisation. The latter constitutes an essential pre-processing step, for which many different algorithms along with numerous software implementations exist. In that context, existing solutions are often model specific, commercial, or depend on commercial back-end software, and allow only a limited or no workflow automation at all. Consequently, a new package for the scientific software and scripting environment R, called lumpR, was developed. lumpR employs an algorithm for hillslope-based landscape discretisation directed to large-scale application via a hierarchical multi-scale approach. The package addresses existing limitations as it is free and open source, easily extendible to other hydrological models, and the workflow can be fully automated. Moreover, it is user-friendly as the direct coupling to a GIS allows for immediate visual inspection and manual adjustment. Sufficient control is furthermore retained via parameter specification and the option to include expert knowledge. Conversely, completely automatic operation also allows for extensive analysis of aspects related to landscape discretisation. In a case study, the application of the package is presented. A sensitivity analysis of the most important discretisation parameters demonstrates its efficient workflow automation. Considering multiple streamflow metrics, the employed model proved reasonably robust to the discretisation parameters. However, parameters determining the sizes of subbasins and hillslopes proved to be more important than the others, including the number of representative hillslopes, the number of attributes employed for the lumping algorithm, and the number of sub-discretisations of the representative hillslopes.
The semiarid northeast of Brazil is one of the most densely populated dryland regions in the world and recurrently affected by severe droughts. Thus, reliable seasonal forecasts of streamflow and reservoir storage are of high value for water managers. Such forecasts can be generated by applying either hydrological models representing underlying processes or statistical relationships exploiting correlations among meteorological and hydrological variables. This work evaluates and compares the performances of seasonal reservoir storage forecasts derived by a process-based hydrological model and a statistical approach.
Driven by observations, both models achieve similar simulation accuracies. In a hindcast experiment, however, the accuracy of estimating regional reservoir storages was considerably lower using the process-based hydrological model, whereas the resolution and reliability of drought event predictions were similar by both approaches. Further investigations regarding the deficiencies of the process-based model revealed a significant influence of antecedent wetness conditions and a higher sensitivity of model prediction performance to rainfall forecast quality.
Within the scope of this study, the statistical model proved to be the more straightforward approach for predictions of reservoir level and drought events at regionally and monthly aggregated scales. However, for forecasts at finer scales of space and time or for the investigation of underlying processes, the costly initialisation and application of a process-based model can be worthwhile. Furthermore, the application of innovative data products, such as remote sensing data, and operational model correction methods, like data assimilation, may allow for an enhanced exploitation of the advanced capabilities of process-based hydrological models.
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.
The results of streamflow trend studies are often characterized by mostly insignificant trends and inexplicable spatial patterns. In our study region, Western Austria, this applies especially for trends of annually averaged runoff. However, analysing the altitudinal aspect, we found that there is a trend gradient from higher-altitude to lower-altitude stations, i.e. a pattern of mostly positive annual trends at higher stations and negative ones at lower stations. At mid-altitudes, the trends are mostly insignificant. Here we hypothesize that the streamflow trends are caused by the following two main processes: on the one hand, melting glaciers produce excess runoff at higher-altitude watersheds. On the other hand, rising temperatures potentially alter hydrological conditions in terms of less snowfall, higher infiltration, enhanced evapotranspiration, etc., which in turn results in decreasing streamflow trends at lower-altitude watersheds. However, these patterns are masked at mid-altitudes because the resulting positive and negative trends balance each other. To support these hypotheses, we attempted to attribute the detected trends to specific causes. For this purpose, we analysed trends of filtered daily streamflow data, as the causes for these changes might be restricted to a smaller temporal scale than the annual one. This allowed for the explicit determination of the exact days of year (DOYs) when certain streamflow trends emerge, which were then linked with the corresponding DOYs of the trends and characteristic dates of other observed variables, e.g. the average DOY when temperature crosses the freezing point in spring. Based on these analyses, an empirical statistical model was derived that was able to simulate daily streamflow trends sufficiently well. Analyses of subdaily streamflow changes provided additional insights. Finally, the present study supports many modelling approaches in the literature which found out that the main drivers of alpine streamflow changes are increased glacial melt, earlier snowmelt and lower snow accumulation in wintertime.
Deforestation is a prominent anthropogenic cause of erosive overland flow and slope instability, boosting rates of soil erosion and concomitant sediment flux. Conventional methods of gauging or estimating post-logging sediment flux often focus on annual timescales but overlook potentially important process response on shorter intervals immediately following timber harvest. We resolve such dynamics with non-parametric quantile regression forests (QRF) based on high-frequency (3 min) discharge measurements and sediment concentration data sampled every 30-60 min in similar-sized (similar to 0.1 km(2)) forested Chilean catchments that were logged during either the rainy or the dry season. The method of QRF builds on the random forest algorithm, and combines quantile regression with repeated random sub-sampling of both cases and predictors. The algorithm belongs to the family of decision-tree classifiers, which allow quantifying relevant predictors in high-dimensional parameter space. We find that, where no logging occurred, similar to 80% of the total sediment load was transported during extremely variable runoff events during only 5% of the monitoring period. In particular, dry-season logging dampened the relative role of these rare, extreme sediment-transport events by increasing load efficiency during more efficient moderate events. We show that QRFs outperform traditional sediment rating curves (SRCs) in terms of accurately simulating short-term dynamics of sediment flux, and conclude that QRF may reliably support forest management recommendations by providing robust simulations of post-logging response of water and sediment fluxes at high temporal resolution.
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.
The 933 km(2) Bengue catchment in northeastern Brazil is characterized by distinct rainy and dry seasons. Precipitation is stored in variously sized reservoirs, which is essential for the local population. In this study, we used TerraSAR-X SM(HH) data for an one-year monitoring of seasonal changes in the reservoir areas from July 2011 to July 2012. The monitoring was based on acquisitions in the ascending pass direction, complemented by occasional descending-pass images. To detect water surface areas, a histogram analysis followed by a global threshold classification was performed, and the results were validated using in situ GPS data. Distinguishing between small reservoirs and similar looking dark areas was difficult. Therefore, we tested several approaches for identifying misclassified areas. An analysis of the surface area dynamics of the reservoirs indicated high spatial and temporal heterogeneities and a large decrease in the total water surface area of the reservoirs in the catchment by approximately 30% within one year.
Owing to average temperature increases of at least twice the global mean, climate change is expected to have strong impacts on local hydrology and climatology in the Alps. Nevertheless, trend analyses of hydro-climatic station data rarely reveal clear patterns concerning climate change signals except in temperature observations. However, trend research has thus far mostly been based on analysing trends of averaged data such as yearly, seasonal or monthly averages and has therefore often not been able to detect the finer temporal dynamics. For this reason, we derived 30-day moving average trends, providing a daily resolution of the timing and magnitude of trends within the seasons. Results are validated by including different time periods. We studied daily observations of mean temperature, liquid and solid precipitation, snow height and runoff in the relatively dry central Alpine region in Tyrol, Austria. Our results indicate that the vast majority of changes are observed throughout spring to early summer, most likely triggered by the strong temperature increase during this season. Temperature, streamflow and snow trends have clearly amplified during recent decades. The overall results are consistent over the entire investigation area and different time periods.
In a study from 2008, Lariviere and colleagues showed, for the field of natural sciences and engineering, that the median age of cited references is increasing over time. This result was considered counterintuitive: with the advent of electronic search engines, online journal issues and open access publications, one could have expected that cited literature is becoming younger. That study has motivated us to take a closer look at the changes in the age distribution of references that have been cited in water resources journals since 1965. Not only could we confirm the findings of Lariviere and colleagues. We were also able to show that the aging is mainly happening in the oldest 10-25% of an average reference list. This is consistent with our analysis of top-cited papers in the field of water resources. Rankings based on total citations since 1965 consistently show the dominance of old literature, including text books and research papers in equal shares. For most top-cited old-timers, citations are still growing exponentially. There is strong evidence that most citations are attracted by publications that introduced methods which meanwhile belong to the standard toolset of researchers and practitioners in the field of water resources. Although we think that this trend should not be overinterpreted as a sign of stagnancy, there might be cause for concern regarding how authors select their references. We question the increasing citation of textbook knowledge as it holds the risk that reference lists become overcrowded, and that the readability of papers deteriorates.
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.
In the field of hydrological modeling, many alternative representations of natural processes exist. Choosing specific process formulations when building a hydrological model is therefore associated with a high degree of ambiguity and subjectivity. In addition, the numerical integration of the underlying differential equations and parametrization of model structures influence model performance. Identifiability analysis may provide guidance by constraining the a priori range of alternatives based on observations. In this work, a flexible simulation environment is used to build an ensemble of semidistributed, process-based hydrological model configurations with alternative process representations, numerical integration schemes, and model parametrizations in an integrated manner. The flexible simulation environment is coupled with an approach for dynamic identifiability analysis. The objective is to investigate the applicability of the framework to identify the most adequate model. While an optimal model configuration could not be clearly distinguished, interesting results were obtained when relating model identifiability with hydro-meteorological boundary conditions. For instance, we tested the Penman-Monteith and Shuttleworth & Wallace evapotranspiration models and found that the former performs better under wet and the latter under dry conditions. Parametrization of model structures plays a dominant role as it can compensate for inadequate process representations and poor numerical solvers. Therefore, it was found that numerical solvers of high order of accuracy do often, though not necessarily, lead to better model performance. The proposed coupled framework proved to be a straightforward diagnostic tool for model building and hypotheses testing and shows potential for more in-depth analysis of process implementations and catchment functioning.
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.
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.
The Southern Pre-Pyrenees experienced a substantial land-use change over the second half of the 20th century owing to the reduction of agricultural activities towards the formation of a more natural forest landscape. The land-use change over the last 50 years with subsequent effects on water and sediment export was modelled with the process-based, spatially semi-distributed WASA-SED model for the meso-scale Canalda catchment in Catalonia, Spain. It was forwarded that the model yielded plausible results for runoff and sediment yield dynamics without the need of calibration, although the model failed to reproduce the shape of the hydrograph and the total discharge of several individual rainstorm events, hence the simulation capabilities are not yet considered sufficient for decision-making purposes for land management. As there are only a very limited amount of measured data available on sediment budgets with altered land-use and climate change settings, the WASA-SED model was used to obtain qualitative estimates on the effects of past and future change scenarios to derive a baseline for hypothesis building and future discussion on the evolution of sediment budgets in such a dryland setting. Simulating the effects of the past land-use change, the model scenarios resulted in a decrease of up to 75% of the annual sediment yield. whereas modelled runoff remained almost constant over the last 50 years. The relative importance of environmental change was evaluated by comparing the impact on sediment export of land-use change, that are driven by socio-economic factors, with climate change projections for changes in the rainfall regime. The modelling results suggest that a 20% decrease in annual rainfall results in a decrease in runoff and sediment yield, thus an ecosystem stabilisation in regard to sediment export which can only be achieved by a substantial land-use change equivalent to a complete afforestation. At the same time, a 20% increase in rainfall causes a large export of water and sediment resources out of the catchment, equivalent to an intensive agricultural use of 100% of the catchment area. For wet years, the effects of agricultural intensification are more pronounced, so that in this case the intensive land-use change has a significantly larger impact on sediment generation than climate change. The WASA-SED model proved capable in quantifying the impacts of actual and potential environmental change, but the reliability of the simulation results is still circumscribed by considerable parameterisation and model uncertainties.
The results of streamflow trend studies are often characterized by mostly insignificant trends and inexplicable spatial patterns. In our study region, Western Austria, this applies especially for trends of annually averaged runoff. However, analysing the altitudinal aspect, we found that there is a trend gradient from higher-altitude to lower-altitude stations, i.e. a pattern of mostly positive annual trends at higher stations and negative ones at lower stations. At midaltitudes, the trends are mostly insignificant. Here we hypothesize that the streamflow trends are caused by the following two main processes: on the one hand, melting glaciers produce excess runoff at higher-altitude watersheds. On the other hand, rising temperatures potentially alter hydrological conditions in terms of less snowfall, higher infiltration, enhanced evapotranspiration, etc., which in turn results in decreasing streamflow trends at lower-altitude watersheds. However, these patterns are masked at mid-altitudes because the resulting positive and negative trends balance each other. To support these hypotheses, we attempted to attribute the detected trends to specific causes. For this purpose, we analysed trends of filtered daily streamflow data, as the causes for these changes might be restricted to a smaller temporal scale than the annual one. This allowed for the explicit determination of the exact days of year (DOYs) when certain streamflow trends emerge, which were then linked with the corresponding DOYs of the trends and characteristic dates of other observed variables, e.g. the average DOY when temperature crosses the freezing point in spring. Based on these analyses, an empirical statistical model was derived that was able to simulate daily streamflow trends sufficiently well. Analyses of subdaily streamflow changes provided additional insights. Finally, the present study supports many modelling approaches in the literature which found out that the main drivers of alpine streamflow changes are increased glacial melt, earlier snowmelt and lower snow accumulation in wintertime.
To support scientifically sound water management in dryland environments a modelling system has been developed for the quantitative assessment of water and sediment fluxes in catchments, transport in the river system, and retention in reservoirs. The spatial scale of interest is the mesoscale because this is the scale most relevant for management of water and land resources.
This modelling system comprises process-oriented hydrological components tailored for dryland characteristics coupled with components comprising hillslope erosion, sediment transport and reservoir deposition processes. The spatial discretization is hierarchically designed according to a multi-scale concept to account for particular relevant process scales. The non-linear and partly intermittent run-off generation and sediment dynamics are dealt with by accounting for connectivity phenomena at the intersections of landscape compartments. The modelling system has been developed by means of data from nested research catchments in NE-Spain and in NE-Brazil.
In the semi-arid NE of Brazil sediment retention along the topography is the main process for sediment retention at all scales, i.e. the sediment delivery is transport limited. This kind of deposition retains roughly 50 to 60 % of eroded sediment, maintaining a similar deposition proportion in all spatial scales investigated. On the other hand, the sediment retained in reservoirs is clearly related to the scale, increasing with catchment area. With increasing area, there are more reservoirs, increasing the possibility of deposition. Furthermore, the area increase also promotes an increase in flow volume, favouring the construction of larger reservoirs, which generally overflow less frequently and retain higher sediment fractions. The second example comprises a highly dynamic Mediterranean catchment in NE-Spain with nested sub-catchments and reveals the full dynamics of hydrological, erosion and deposition features. The run-off modelling performed well with only some overestimation during low-flow periods due to the neglect of water losses along the river. The simulated peaks in sediment flux are reproduced well, while low-flow sediment transport is less well captured, due to the disregard of sediment remobilization in the riverbed during low flow.
This combined observation and modelling study deepened the understanding of hydro-sedimentological systems characterized by flashy run-off generation and by erosion and sediment transport pulses through the different landscape compartments. The connectivity between the different landscape compartments plays a very relevant role, regarding both the total mass of water and sediment transport and the transport time through the catchment.
The characteristics of a landscape pose essential factors for hydrological processes. Therefore, an adequate representation of the landscape of a catchment in hydrological models is vital. However, many of such models exist differing, amongst others, in spatial concept and discretisation. The latter constitutes an essential pre-processing step, for which many different algorithms along with numerous software implementations exist. In that context, existing solutions are often model specific, commercial, or depend on commercial back-end software, and allow only a limited or no workflow automation at all.
Consequently, a new package for the scientific software and scripting environment R, called lumpR, was developed. lumpR employs an algorithm for hillslope-based landscape discretisation directed to large-scale application via a hierarchical multi-scale approach. The package addresses existing limitations as it is free and open source, easily extendible to other hydrological models, and the workflow can be fully automated. Moreover, it is user-friendly as the direct coupling to a GIS allows for immediate visual inspection and manual adjustment. Sufficient control is furthermore retained via parameter specification and the option to include expert knowledge. Conversely, completely automatic operation also allows for extensive analysis of aspects related to landscape discretisation.
In a case study, the application of the package is presented. A sensitivity analysis of the most important discretisation parameters demonstrates its efficient workflow automation. Considering multiple streamflow metrics, the employed model proved reasonably robust to the discretisation parameters. However, parameters determining the sizes of subbasins and hillslopes proved to be more important than the others, including the number of representative hillslopes, the number of attributes employed for the lumping algorithm, and the number of sub-discretisations of the representative hillslopes.
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.
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.
Knowledge of sediment sources is a prerequisite for sustainable management practices and may furthermore improve our understanding of water and sediment fluxes. Investigations have shown that a number of characteristic soil properties can be used as "fingerprints" to trace back the sources of river sediments. Spectral properties have recently been successfully used as such characteristics in fingerprinting studies. Despite being less labour-intensive than geochemical analyses, for example, spectroscopy allows measurements of small amounts of sediment material (> 60 mg), thus enabling inexpensive analyses even of intra-event variability. The focus of this study is on the examination of spectral properties of fluvial sediment samples to detect changes in source contributions, both between and within individual flood events.
Sediment samples from the following three different origins were collected in the Isabena catchment (445 km(2)) in the central Spanish Pyrenees: (1) soil samples from the main potential source areas, (2) stored fine sediment from the channel bed once each season in 2011 and (3) suspended sediment samples during four flood events in autumn 2011 and spring 2012 at the catchment outlet as well as at several subcatchment outlets. All samples were dried and measured for spectral properties in the laboratory using an ASD spectroradiometer. Colour parameters and physically based features (e.g. organic carbon, iron oxide and clay content) were calculated from the spectra. Principal component analyses (PCA) were applied to all three types of samples to determine natural clustering of samples, and a mixing model was applied to determine source contributions.
We found that fine sediment stored in the river bed seems to be mainly influenced by grain size and seasonal variability, while sampling location-and thus the effect of individual tributaries or subcatchments-seem to be of minor importance. Suspended sediment sources were found to vary between, as well as within, flood events; although badlands were always the major source. Forests and grasslands contributed little (< 10 %), and other sources (not further determinable) contributed up to 40 %. The analyses further suggested that sediment sources differ among the subcatchments and that subcatchments comprising relatively large proportions of badlands contributed most to the four flood events analyzed.
Spectral fingerprints provide a rapid and cost-efficient alternative to conventional fingerprint properties. However, a combination of spectral and conventional fingerprint properties could potentially permit discrimination of a larger number of source types.
Urban surface runoff management via best management practices (BMP) and low impact development (LID) has earned significant recognition owing to positive environmental and ecological impacts. However, due to the complexity of the parameters involved, the estimation of LID efficiency in attenuating the urban surface runoff at the watershed scale is challenging. A planning analysis of employing Green Roofs and Infiltration Trenches as BMPs/LIDs practices for urban surface runoff control is presented in this study. A multi-objective optimization decision-making framework is established by coupling SWMM (Storm Water Management Model) with NSGA-II models to check the performance of BMPs/LIDs concerning the cost-benefit analysis of LID at the watershed scale. Two urbanized areas belonging to Central Delhi in India were used as case studies. The results showed that the SWMM model is useful in simulating optimization problems for managing urban surface runoff. The optimum scenarios efficiently minimized the urban runoff volume while maintaining the BMPs/LIDs implementation costs and size. With BMPs/LIDs implementation, the reduction in runoff volume increases as expenses increase initially; however, there is no noticeable reduction in flood volume after a certain threshold. Contrasted with the haphazard arrangement of BMPs/LIDs, the proposed approach demonstrates 22%-24% runoff reductions for the same expenditures in watershed 1 and 23%-26% in watershed 2. The result of the study provides insights into planning and management of the urban surface runoff control with LID practices. The proposed framework assists the hydrologists in optimum selection and placements of BMPs/LIDs practices to acquire the most extreme ecological advantages with the least expenses.
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.
Modelers can improve a model by addressing the causes for the model errors (data errors and structural errors). This leads to implementing model enhancements (MEs), for example, meteorological data based on more monitoring stations, improved calibration data, and/or modifications in process formulations. However, deciding on which MEs to implement remains a matter of expert knowledge. After implementing multiple MEs, any improvement in model performance is not easily attributed, especially when considering different objectives or aspects of this improvement (e.g., better dynamics vs. reduced bias). We present an approach for comparing the effect of multiple MEs based on real observations and considering multiple objectives (MMEMO). A stepwise selection approach and structured plots help to address the multidimensionality of the problem. Tailored analyses allow a differentiated view on the effect of MEs and their interactions. MMEMO is applied to a case study employing the mesoscale hydro-sedimentological model WASA-SED for the Mediterranean-mountainous Isabena catchment, northeast Spain. The investigated seven MEs show diverse effects: some MEs (e.g., rainfall data) cause improvements for most objectives, while other MEs (e.g., land use data) only affect a few objectives or even decrease model performance. Interaction of MEs was observed for roughly half of the MEs, confirming the need to address them in the analysis. Calibration and increasing the temporal resolution showed by far stronger impact than any of the other MEs. The proposed framework can be adopted in other studies to analyze the effect of MEs and, thus, facilitate the identification and implementation of the most promising MEs for comparable cases.
A comprehensive hydro-sedimentological dataset for the Isabena catchment, northeastern (NE) Spain, for the period 2010-2018 is presented to analyse water and sediment fluxes in a Mediterranean mesoscale catchment. The dataset includes rainfall data from 12 rain gauges distributed within the study area complemented by meteorological data of 12 official meteo-stations. It comprises discharge data derived from water stage measurements as well as suspended sediment concentrations (SSCs) at six gauging stations of the River Isabena and its sub-catchments. Soil spectroscopic data from 351 suspended sediment samples and 152 soil samples were collected to characterize sediment source regions and sediment properties via fingerprinting analyses. The Isabena catchment (445 km(2)) is located in the southern central Pyrenees ranging from 450 m to 2720 m a.s.l.; together with a pronounced topography, this leads to distinct temperature and precipitation gradients. The River Isabena shows marked discharge variations and high sediment yields causing severe siltation problems in the downstream Barasona Reservoir. The main sediment source is badland areas located on Eocene marls that are well connected to the river network. The dataset features a comprehensive set of variables in a high spatial and temporal resolution suitable for the advanced process understanding of water and sediment fluxes, their origin and connectivity and sediment budgeting and for the evaluation and further development of hydro-sedimentological models in Mediterranean mesoscale mountainous catchments.
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.