TY - JOUR A1 - Bronstert, Axel T1 - The role of infiltration conditions for storm runoff generation at the hillslope and small catchment scale Y1 - 2001 ER - TY - JOUR A1 - Krol, Marten S. A1 - Jaeger, Annekathrin A1 - Bronstert, Axel A1 - Krywkow, J. T1 - The Semi-arid Integrated Model (SIM), a regional integrated model assessing water availability, vulnerability of ecosystems and society in NE-Brazil Y1 - 2001 ER - TY - JOUR A1 - Conradt, Tobias A1 - Wechsung, F. A1 - Bronstert, Axel T1 - Three perceptions of the evapotranspiration landscape comparing spatial patterns from a distributed hydrological model, remotely sensed surface temperatures, and sub-basin water balances JF - Hydrology and earth system sciences : HESS N2 - A problem encountered by many distributed hydrological modelling studies is high simulation errors at interior gauges when the model is only globally calibrated at the outlet. We simulated river runoff in the Elbe River basin in central Europe (148 268 km(2)) with the semi-distributed eco-hydrological model SWIM (Soil and Water Integrated Model). While global parameter optimisation led to Nash-Sutcliffe efficiencies of 0.9 at the main outlet gauge, comparisons with measured runoff series at interior points revealed large deviations. Therefore, we compared three different strategies for deriving sub-basin evapotranspiration: (1) modelled by SWIM without any spatial calibration, (2) derived from remotely sensed surface temperatures, and (3) calculated from long-term precipitation and discharge data. The results show certain consistencies between the modelled and the remote sensing based evapotranspiration rates, but there seems to be no correlation between remote sensing and water balance based estimations. Subsequent analyses for single sub-basins identify amongst others input weather data and systematic error amplification in inter-gauge discharge calculations as sources of uncertainty. The results encourage careful utilisation of different data sources for enhancements in distributed hydrological modelling. Y1 - 2013 U6 - https://doi.org/10.5194/hess-17-2947-2013 SN - 1027-5606 VL - 17 IS - 7 SP - 2947 EP - 2966 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Fernandez-Palomino, Carlos Antonio A1 - Hattermann, Fred A1 - Krysanova, Valentina A1 - Vega-Jacome, Fiorella A1 - Bronstert, Axel T1 - Towards a more consistent eco-hydrological modelling through multi-objective calibration BT - a case study in the Andean Vilcanota River basin, Perú JF - Hydrological sciences journal = Journal des sciences hydrologiques N2 - 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. KW - Andes KW - eco-hydrology KW - SWAT KW - hydrological signatures KW - remote sensing KW - equifinality Y1 - 2020 U6 - https://doi.org/10.1080/02626667.2020.1846740 SN - 0262-6667 SN - 2150-3435 VL - 66 IS - 1 SP - 59 EP - 74 PB - Routledge, Taylor & Francis Group CY - Abingdon ER - TY - JOUR A1 - Bürger, Gerd A1 - Heistermann, Maik A1 - Bronstert, Axel T1 - Towards subdaily rainfall disaggregation via Clausius-Clapeyron JF - Journal of hydrometeorology N2 - Two lines of research are combined in this study: first, the development of tools for the temporal disaggregation of precipitation, and second, some newer results on the exponential scaling of heavy short-term precipitation with temperature, roughly following the Clausius-Clapeyron (CC) relation. Having no extra temperature dependence, the traditional disaggregation schemes are shown to lack the crucial CC-type temperature dependence. The authors introduce a proof-of-concept adjustment of an existing disaggregation tool, the multiplicative cascade model of Olsson, and show that, in principal, it is possible to include temperature dependence in the disaggregation step, resulting in a fairly realistic temperature dependence of the CC type. They conclude by outlining the main calibration steps necessary to develop a full-fledged CC disaggregation scheme and discuss possible applications. Y1 - 2014 U6 - https://doi.org/10.1175/JHM-D-13-0161.1 SN - 1525-755X SN - 1525-7541 VL - 15 IS - 3 SP - 1303 EP - 1311 PB - American Meteorological Soc. CY - Boston ER - TY - JOUR A1 - Seleem, Omar A1 - Ayzel, Georgy A1 - Costa Tomaz de Souza, Arthur A1 - Bronstert, Axel A1 - Heistermann, Maik T1 - Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany JF - Geomatics, natural hazards and risk N2 - 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. KW - Urban pluvial flood susceptibility KW - convolutional neural network KW - deep KW - learning KW - random forest KW - support vector machine KW - spatial resolution; KW - flood predictors Y1 - 2022 U6 - https://doi.org/10.1080/19475705.2022.2097131 SN - 1947-5705 SN - 1947-5713 VL - 13 IS - 1 SP - 1640 EP - 1662 PB - Taylor & Francis CY - London ER - TY - JOUR A1 - Seleem, Omar A1 - Ayzel, Georgy A1 - Bronstert, Axel A1 - Heistermann, Maik T1 - Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany JF - Natural Hazards and Earth System Sciences N2 - 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. Y1 - 2023 U6 - https://doi.org/10.5194/nhess-23-809-2023 SN - 1684-9981 SN - 1561-8633 VL - 23 IS - 2 SP - 809 EP - 822 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Bronstert, Axel A1 - Bardossy, Andras T1 - Uncertainty of runoff modelling at the hillslope scale due to temporal variations of rainfall intensity Y1 - 2003 ER - TY - JOUR A1 - Heistermann, Maik A1 - Crisologo, Irene A1 - Abon, Catherine Cristobal A1 - Racoma, B. A. A1 - Jacobi, S. A1 - Servando, N. T. A1 - David, C. P. C. A1 - Bronstert, Axel T1 - Using the new Philippine radar network to reconstruct the Habagat of August 2012 monsoon event around Metropolitan Manila JF - Natural hazards and earth system sciences N2 - 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. Y1 - 2013 U6 - https://doi.org/10.5194/nhess-13-653-2013 SN - 1561-8633 VL - 13 IS - 3 SP - 653 EP - 657 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Bronstert, Axel A1 - Engel, H. T1 - Veränderung der Abflüsse JF - Warnsignal Klima - genug Wasser für alle? : wissenschaftliche Fakten Y1 - 2005 SN - 978-3-9809668-0-1 SP - 175 EP - 181 PB - Wissenschaftliche Auswertungen CY - Hamburg ER -