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 - Ayzel, Georgy A1 - Heistermann, Maik T1 - The effect of calibration data length on the performance of a conceptual hydrological model versus LSTM and GRU BT - a case study for six basins from the CAMELS dataset JF - Computers & geosciences : an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets ; an official journal of the International Association for Mathematical Geology N2 - We systematically explore the effect of calibration data length on the performance of a conceptual hydrological model, GR4H, in comparison to two Artificial Neural Network (ANN) architectures: Long Short-Term Memory Networks (LSTM) and Gated Recurrent Units (GRU), which have just recently been introduced to the field of hydrology. We implemented a case study for six river basins across the contiguous United States, with 25 years of meteorological and discharge data. Nine years were reserved for independent validation; two years were used as a warm-up period, one year for each of the calibration and validation periods, respectively; from the remaining 14 years, we sampled increasing amounts of data for model calibration, and found pronounced differences in model performance. While GR4H required less data to converge, LSTM and GRU caught up at a remarkable rate, considering their number of parameters. Also, LSTM and GRU exhibited the higher calibration instability in comparison to GR4H. These findings confirm the potential of modern deep-learning architectures in rainfall runoff modelling, but also highlight the noticeable differences between them in regard to the effect of calibration data length. KW - Artificial neural networks KW - Calibration KW - Deep learning KW - Rainfall-runoff KW - modelling Y1 - 2021 U6 - https://doi.org/10.1016/j.cageo.2021.104708 SN - 0098-3004 SN - 1873-7803 VL - 149 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Bronstert, Axel A1 - Crisologo, Irene A1 - Heistermann, Maik A1 - Öztürk, Ugur A1 - Vogel, Kristin A1 - Wendi, Dadiyorto T1 - Flash-floods: more often, more severe, more damaging? BT - An analysis of hydro-geo-environmental conditions and anthropogenic impacts T2 - Climate change, hazards and adaptation options: handling the impacts of a changing climate N2 - In recent years, urban and rural flash floods in Europe and abroad have gained considerable attention because of their sudden occurrence, severe material damages and even danger to life of inhabitants. This contribution addresses questions about possibly changing environmental conditions which might have altered the occurrence frequencies of such events and their consequences. We analyze the following major fields of environmental changes. Altered high intensity rain storm conditions, as a consequence of regionalwarming; Possibly altered runoff generation conditions in response to high intensity rainfall events; Possibly altered runoff concentration conditions in response to the usage and management of the landscape, such as agricultural, forest practices or rural roads; Effects of engineering measures in the catchment, such as retention basins, check dams, culverts, or river and geomorphological engineering measures. We take the flash-flood in Braunsbach, SW-Germany, as an example, where a particularly concise flash flood event occurred at the end of May 2016. This extreme cascading natural event led to immense damage in this particular village. The event is retrospectively analyzed with regard to meteorology, hydrology, geomorphology and damage to obtain a quantitative assessment of the processes and their development. The results show that it was a very rare rainfall event with extreme intensities, which in combination with catchment properties and altered environmental conditions led to extreme runoff, extreme debris flow and immense damages. Due to the complex and interacting processes, no single flood cause can be identified, since only the interplay of those led to such an event. We have shown that environmental changes are important, but-at least for this case study-even natural weather and hydrologic conditions would still have resulted in an extreme flash flood event. KW - Flash flood KW - Climate change KW - Extreme rainfall KW - Anthropogenic impacts Y1 - 2020 SN - 978-3-030-37425-9 SN - 978-3-030-37424-2 U6 - https://doi.org/10.1007/978-3-030-37425-9_12 SN - 1610-2010 SP - 225 EP - 244 PB - Springer CY - Cham ER - TY - JOUR A1 - Crisologo, Irene A1 - Heistermann, Maik T1 - Using ground radar overlaps to verify the retrieval of calibration bias estimates from spaceborne platforms JF - Atmospheric measurement techniques : an interactive open access journal of the European Geosciences Union N2 - Many institutions struggle to tap into the potential of their large archives of radar reflectivity: these data are often affected by miscalibration, yet the bias is typically unknown and temporally volatile. Still, relative calibration techniques can be used to correct the measurements a posteriori. For that purpose, the usage of spaceborne reflectivity observations from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) platforms has become increasingly popular: the calibration bias of a ground radar (GR) is estimated from its average reflectivity difference to the spaceborne radar (SR). Recently, Crisologo et al. (2018) introduced a formal procedure to enhance the reliability of such estimates: each match between SR and GR observations is assigned a quality index, and the calibration bias is inferred as a quality-weighted average of the differences between SR and GR. The relevance of quality was exemplified for the Subic S-band radar in the Philippines, which is greatly affected by partial beam blockage. The present study extends the concept of quality-weighted averaging by accounting for path-integrated attenuation (PIA) in addition to beam blockage. This extension becomes vital for radars that operate at the C or X band. Correspondingly, the study setup includes a C-band radar that substantially overlaps with the S-band radar. Based on the extended quality-weighting approach, we retrieve, for each of the two ground radars, a time series of calibration bias estimates from suitable SR overpasses. As a result of applying these estimates to correct the ground radar observations, the consistency between the ground radars in the region of overlap increased substantially. Furthermore, we investigated if the bias estimates can be interpolated in time, so that ground radar observations can be corrected even in the absence of prompt SR overpasses. We found that a moving average approach was most suitable for that purpose, although limited by the absence of explicit records of radar maintenance operations. Y1 - 2020 U6 - https://doi.org/10.5194/amt-13-645-2020 SN - 1867-1381 SN - 1867-8548 VL - 13 IS - 2 SP - 645 EP - 659 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Voit, Paul A1 - Heistermann, Maik T1 - A new index to quantify the extremeness of precipitation across scales JF - NHESS - Natural Hazards and Earth System Sciences N2 - Quantifying the extremeness of heavy precipitation allows for the comparison of events. Conventional quantitative indices, however, typically neglect the spatial extent or the duration, while both are important to understand potential impacts. In 2014, the weather extremity index (WEI) was suggested to quantify the extremeness of an event and to identify the spatial and temporal scale at which the event was most extreme. However, the WEI does not account for the fact that one event can be extreme at various spatial and temporal scales. To better understand and detect the compound nature of precipitation events, we suggest complementing the original WEI with a “cross-scale weather extremity index” (xWEI), which integrates extremeness over relevant scales instead of determining its maximum. Based on a set of 101 extreme precipitation events in Germany, we outline and demonstrate the computation of both WEI and xWEI. We find that the choice of the index can lead to considerable differences in the assessment of past events but that the most extreme events are ranked consistently, independently of the index. Even then, the xWEI can reveal cross-scale properties which would otherwise remain hidden. This also applies to the disastrous event from July 2021, which clearly outranks all other analyzed events with regard to both WEI and xWEI. While demonstrating the added value of xWEI, we also identify various methodological challenges along the required computational workflow: these include the parameter estimation for the extreme value distributions, the definition of maximum spatial extent and temporal duration, and the weighting of extremeness at different scales. These challenges, however, also represent opportunities to adjust the retrieval of WEI and xWEI to specific user requirements and application scenarios. Y1 - 2022 U6 - https://doi.org/10.5194/nhess-22-2791-2022 SN - 1684-9981 VL - 22 SP - 2791 EP - 2805 PB - Copernicus CY - Katlenburg-Lindau ET - 8 ER - TY - JOUR A1 - Costa Tomaz de Souza, Arthur A1 - Ayzel, Georgy A1 - Heistermann, Maik T1 - Quantifying the location error of precipitation nowcasts JF - Advances in meteorology N2 - In precipitation nowcasting, it is common to track the motion of precipitation in a sequence of weather radar images and to extrapolate this motion into the future. The total error of such a prediction consists of an error in the predicted location of a precipitation feature and an error in the change of precipitation intensity over lead time. So far, verification measures did not allow isolating the extent of location errors, making it difficult to specifically improve nowcast models with regard to location prediction. In this paper, we introduce a framework to directly quantify the location error. To that end, we detect and track scale-invariant precipitation features (corners) in radar images. We then consider these observed tracks as the true reference in order to evaluate the performance (or, inversely, the error) of any model that aims to predict the future location of a precipitation feature. Hence, the location error of a forecast at any lead time Delta t ahead of the forecast time t corresponds to the Euclidean distance between the observed and the predicted feature locations at t + Delta t. Based on this framework, we carried out a benchmarking case study using one year worth of weather radar composites of the German Weather Service. We evaluated the performance of four extrapolation models, two of which are based on the linear extrapolation of corner motion from t - 1 to t (LK-Lin1) and t - 4 to t (LK-Lin4) and the other two are based on the Dense Inverse Search (DIS) method: motion vectors obtained from DIS are used to predict feature locations by linear (DIS-Lin1) and Semi-Lagrangian extrapolation (DIS-Rot1). Of those four models, DIS-Lin1 and LK-Lin4 turned out to be the most skillful with regard to the prediction of feature location, while we also found that the model skill dramatically depends on the sinuosity of the observed tracks. The dataset of 376,125 detected feature tracks in 2016 is openly available to foster the improvement of location prediction in extrapolation-based nowcasting models. KW - inuosity Y1 - 2020 U6 - https://doi.org/10.1155/2020/8841913 SN - 1687-9309 SN - 1687-9317 VL - 2020 PB - Hindawi CY - London ER - TY - JOUR A1 - Heistermann, Maik A1 - Bogena, Heye A1 - Francke, Till A1 - Güntner, Andreas A1 - Jakobi, Jannis A1 - Rasche, Daniel A1 - Schrön, Martin A1 - Döpper, Veronika A1 - Fersch, Benjamin A1 - Groh, Jannis A1 - Patil, Amol A1 - Pütz, Thomas A1 - Reich, Marvin A1 - Zacharias, Steffen A1 - Zengerle, Carmen A1 - Oswald, Sascha T1 - Soil moisture observation in a forested headwater catchment: combining a dense cosmic-ray neutron sensor network with roving and hydrogravimetry at the TERENO site Wüstebach JF - Earth System Science Data (ESSD) N2 - Cosmic-ray neutron sensing (CRNS) has become an effective method to measure soil moisture at a horizontal scale of hundreds of metres and a depth of decimetres. Recent studies proposed operating CRNS in a network with overlapping footprints in order to cover root-zone water dynamics at the small catchment scale and, at the same time, to represent spatial heterogeneity. In a joint field campaign from September to November 2020 (JFC-2020), five German research institutions deployed 15 CRNS sensors in the 0.4 km2 Wüstebach catchment (Eifel mountains, Germany). The catchment is dominantly forested (but includes a substantial fraction of open vegetation) and features a topographically distinct catchment boundary. In addition to the dense CRNS coverage, the campaign featured a unique combination of additional instruments and techniques: hydro-gravimetry (to detect water storage dynamics also below the root zone); ground-based and, for the first time, airborne CRNS roving; an extensive wireless soil sensor network, supplemented by manual measurements; and six weighable lysimeters. Together with comprehensive data from the long-term local research infrastructure, the published data set (available at https://doi.org/10.23728/b2share.756ca0485800474e9dc7f5949c63b872; Heistermann et al., 2022) will be a valuable asset in various research contexts: to advance the retrieval of landscape water storage from CRNS, wireless soil sensor networks, or hydrogravimetry; to identify scale-specific combinations of sensors and methods to represent soil moisture variability; to improve the understanding and simulation of land–atmosphere exchange as well as hydrological and hydrogeological processes at the hillslope and the catchment scale; and to support the retrieval of soil water content from airborne and spaceborne remote sensing platforms. Y1 - 2022 U6 - https://doi.org/10.5194/essd-14-2501-2022 SN - 1866-3516 VL - 14 SP - 2501 EP - 2519 PB - Copernicus CY - Katlenburg-Lindau 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 - GEN A1 - Petrow, Theresia A1 - Heistermann, Maik A1 - Bronstert, Axel T1 - Analysis of Flash Floods in Germany T2 - Hydrologie und Wasserbewirtschaftung Y1 - 2017 SN - 1439-1783 VL - 61 SP - 212 EP - 212 PB - Bundesanst. für Gewässerkunde CY - Koblenz ER - TY - JOUR A1 - Vormoor, Klaus Josef A1 - Heistermann, Maik A1 - Bronstert, Axel A1 - Lawrence, Deborah T1 - Hydrological model parameter (in)stability BT - "crash testing" the HBV model under contrasting flood seasonality conditions JF - Hydrological sciences journal = Journal des sciences hydrologiques N2 - This paper investigates the transferability of calibrated HBV model parameters under stable and contrasting conditions in terms of flood seasonality and flood generating processes (FGP) in five Norwegian catchments with mixed snowmelt/rainfall regimes. We apply a series of generalized (differential) split-sample tests using a 6-year moving window over (i) the entire runoff observation periods, and (ii) two subsets of runoff observations distinguished by the seasonal occurrence of annual maximum floods during either spring or autumn. The results indicate a general model performance loss due to the transfer of calibrated parameters to independent validation periods of -5 to -17%, on average. However, there is no indication that contrasting flood seasonality exacerbates performance losses, which contradicts the assumption that optimized parameter sets for snowmelt-dominated floods (during spring) perform particularly poorly on validation periods with rainfall-dominated floods (during autumn) and vice versa. KW - hydrological modelling KW - flood seasonality KW - differential split-sample test KW - flood generating processes KW - Nordic catchments Y1 - 2018 U6 - https://doi.org/10.1080/02626667.2018.1466056 SN - 0262-6667 SN - 2150-3435 VL - 63 IS - 7 SP - 991 EP - 1007 PB - Routledge, Taylor & Francis Group CY - Abingdon ER -