@misc{RottlerFranckeBuergeretal.2020, author = {Rottler, Erwin and Francke, Till and B{\"u}rger, Gerd and Bronstert, Axel}, title = {Long-term changes in central European river discharge for 1869-2016}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {4}, issn = {1866-8372}, doi = {10.25932/publishup-51776}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-517763}, pages = {22}, year = {2020}, abstract = {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.}, language = {en} } @article{MtilatilaBronstertShresthaetal.2020, author = {Mtilatila, Lucy Mphatso Ng'ombe and Bronstert, Axel and Shrestha, Pallav and Kadewere, Peter and Vormoor, Klaus Josef}, title = {Susceptibility of water resources and hydropower production to climate change in the tropics}, series = {Hydrology : open access journal}, volume = {7}, journal = {Hydrology : open access journal}, number = {3}, publisher = {MDPI}, address = {Basel}, issn = {2306-5338}, doi = {10.3390/hydrology7030054}, pages = {26}, year = {2020}, abstract = {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.}, language = {en} } @article{RottlerVormoorFranckeetal.2021, author = {Rottler, Erwin and Vormoor, Klaus Josef and Francke, Till and Warscher, Michael and Strasser, Ulrich and Bronstert, Axel}, title = {Elevation-dependent compensation effects in snowmelt in the Rhine River Basin upstream gauge Basel}, series = {Hydrology research : an international journal / Nordic Association of Hydrology ; British Hydrological Society}, volume = {52}, journal = {Hydrology research : an international journal / Nordic Association of Hydrology ; British Hydrological Society}, number = {2}, publisher = {IWA Publ.}, address = {London}, issn = {2224-7955}, doi = {10.2166/nh.2021.092}, pages = {536 -- 557}, year = {2021}, abstract = {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.}, language = {en} } @article{MtilatilaBronstertVormoor2022, author = {Mtilatila, Lucy Mphatso Ng'ombe and Bronstert, Axel and Vormoor, Klaus Josef}, title = {Temporal evaluation and projections of meteorological droughts in the Greater Lake Malawi Basin, Southeast Africa}, series = {Frontiers in water}, volume = {4}, journal = {Frontiers in water}, publisher = {Frontiers Media}, address = {Lausanne}, issn = {2624-9375}, doi = {10.3389/frwa.2022.1041452}, pages = {16}, year = {2022}, abstract = {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.}, language = {en} } @article{BuergerPfisterBronstert2021, author = {B{\"u}rger, Gerd and Pfister, Angela and Bronstert, Axel}, title = {Zunehmende Starkregenintensit{\"a}ten als Folge der Klimaerw{\"a}rmung}, series = {Hydrologie und Wasserbewirtschaftung : HyWa = Hydrology and water resources management, Germany / Hrsg.: Fachverwaltungen des Bundes und der L{\"a}nder}, volume = {65}, journal = {Hydrologie und Wasserbewirtschaftung : HyWa = Hydrology and water resources management, Germany / Hrsg.: Fachverwaltungen des Bundes und der L{\"a}nder}, number = {6}, publisher = {Bundesanst. f{\"u}r Gew{\"a}sserkunde}, address = {Koblenz}, issn = {1439-1783}, doi = {10.5675/HyWa_2021.6_1}, pages = {262 -- 271}, year = {2021}, abstract = {Extreme rainfall events of short duration in the range of hours and below are increasingly coming into focus due to the resulting damage from flash floods and also due to their possible intensification by anthropogenic climate change. The current study investigates possible trends in heavy rainfall intensities for stations from Swiss and Austrian alpine regions as well as for the Emscher-Lippe area in North Rhine-Westphalia on the basis of partly very long (> 50 years) and temporally highly resolved time series (<= 15 minutes). It becomes clear that there is an increase in extreme rainfall intensities, which can be well explained by the warming of the regional climate: the analyses of long-term trends in exceedance counts and return levels show considerable uncertainties, but are in the order of 30 \% increase per century. In addition, based on an "average" climate simulation for the 21st century, this paper describes a projection for extreme precipitation intensities at very high temporal resolution for a number of stations in the Emscher-Lippe region. A coupled spatial and temporal "downscaling" is applied, the key innovation of which is the consideration of the dependence of local rainfall intensity on air temperature. This procedure involves two steps: First, large-scale climate fields at daily resolution are statistically linked by regression to station temperature and precipitation values (spatial downscaling). In the second step, these station values are disaggregated to a temporal resolution of 10 minutes using a so-called multiplicative stochastic cascade model (MC) (temporal downscaling). The novel, temperature-sensitive variant additionally considers air temperature as an explanatory variable for precipitation intensities. Thus, the higher atmospheric moisture content expected with warming, which results from the Clausius-Clapeyron (CC) relationship, is included in the temporal downscaling.
For the statistical evaluation of the extreme short-term precipitation, the upper quantiles (99.9 \%), exceedance counts (P > 5mm), and 3-yr return levels of the <= 15-min duration step has been used. Only by adding temperature is the observed temperature observed of the extreme quantiles ("CC scaling") well reproduced. When comparing observed data and present-day simulations of the model cascade, the temperature-sensitive procedure shows consistent results. Compared to trends in recent decades, similar or even larger increases in extreme intensities are projected for the future. This is remarkable in that these appear to be driven primarily by local temperature, as the projected trends in daily precipitation values are negligible for this region.}, language = {de} } @misc{SeleemAyzelBronstertetal.2023, author = {Seleem, Omar and Ayzel, Georgy and Bronstert, Axel and Heistermann, Maik}, title = {Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {1323}, issn = {1866-8372}, doi = {10.25932/publishup-58916}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-589168}, pages = {809 -- 822}, year = {2023}, abstract = {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.}, language = {en} } @article{SeleemAyzelBronstertetal.2023, author = {Seleem, Omar and Ayzel, Georgy and Bronstert, Axel and Heistermann, Maik}, title = {Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany}, series = {Natural Hazards and Earth System Sciences}, volume = {23}, journal = {Natural Hazards and Earth System Sciences}, number = {2}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1684-9981}, doi = {10.5194/nhess-23-809-2023}, pages = {809 -- 822}, year = {2023}, abstract = {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.}, language = {en} } @article{BronstertBuergerPfister2021, author = {Bronstert, Axel and B{\"u}rger, Gerhard and Pfister, Angela}, title = {Vorhersage und Projektion von Sturzfluten - Vorwort}, series = {Hydrologie und Wasserbewirtschaftung : HyWa = Hydrology and water resources management, Germany / Hrsg.: Fachverwaltungen des Bundes und der L{\"a}nder}, volume = {65}, journal = {Hydrologie und Wasserbewirtschaftung : HyWa = Hydrology and water resources management, Germany / Hrsg.: Fachverwaltungen des Bundes und der L{\"a}nder}, number = {6}, publisher = {Bundesanst. f{\"u}r Gew{\"a}sserkunde, BfG}, address = {Koblenz}, issn = {1439-1783}, pages = {260 -- 261}, year = {2021}, language = {de} } @misc{SeleemAyzelCostaTomazdeSouzaetal.2022, author = {Seleem, Omar and Ayzel, Georgy and Costa Tomaz de Souza, Arthur and Bronstert, Axel and Heistermann, Maik}, title = {Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {1297}, issn = {1866-8372}, doi = {10.25932/publishup-57680}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-576806}, pages = {1640 -- 1662}, year = {2022}, abstract = {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.}, language = {en} } @article{SchmidtFranckeRottleretal.2022, author = {Schmidt, Lena Katharina and Francke, Till and Rottler, Erwin and Blume, Theresa and Sch{\"o}ber, Johannes and Bronstert, Axel}, title = {Suspended sediment and discharge dynamics in a glaciated alpine environment}, series = {Earth surface dynamics}, volume = {10}, journal = {Earth surface dynamics}, number = {3}, publisher = {Copernicus Publications}, address = {G{\"o}ttingen}, issn = {2196-632X}, doi = {10.5194/esurf-10-653-2022}, pages = {653 -- 669}, year = {2022}, abstract = {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 {\"O}tztal 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, S{\"o}lden 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.}, language = {en} }