@article{DidovetsKrysanovaBuergeretal.2019, author = {Didovets, Iulii and Krysanova, Valentina and B{\"u}rger, Gerd and Snizhko, Sergiy and Balabukh, Vira and Bronstert, Axel}, title = {Climate change impact on regional floods in the Carpathian region}, series = {Journal of hydrology : Regional studies}, volume = {22}, journal = {Journal of hydrology : Regional studies}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2214-5818}, doi = {10.1016/j.ejrh.2019.01.002}, pages = {14}, year = {2019}, abstract = {Study region: Tisza and Prut catchments, originating on the slopes of the Carpathian mountains. Study focus: The study reported here investigates (i) climate change impacts on flood risk in the region, and (ii) uncertainty related to hydrological modelling, downscaling techniques and climate projections. The climate projections used in the study were derived from five GCMs, downscaled either dynamically with RCMs or with the statistical downscaling model XDS. The resulting climate change scenarios were applied to drive the eco-hydrological model SWIM, which was calibrated and validated for the catchments in advance using observed climate and hydrological data. The changes in the 30-year flood hazards and 98 and 95 percentiles of discharge were evaluated for the far future period (2071-2100) in comparison with the reference period (1981-2010). New hydrological insights for the region: The majority of model outputs under RCP 4.5 show a small to strong increase of the 30-year flood level in the Tisza ranging from 4.5\% to 62\%, and moderate increase in the Prut ranging from 11\% to 22\%. The impact results under RCP 8.5 are more uncertain with changes in both directions due to high uncertainties in GCM-RCM climate projections, downscaling methods and the low density of available climate stations.}, language = {en} } @article{vonSpechtOeztuerkVehetal.2019, author = {von Specht, Sebastian and {\"O}zt{\"u}rk, Ugur and Veh, Georg and Cotton, Fabrice and Korup, Oliver}, title = {Effects of finite source rupture on landslide triggering}, series = {Solid earth}, volume = {10}, journal = {Solid earth}, number = {2}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1869-9510}, doi = {10.5194/se-10-463-2019}, pages = {463 -- 486}, year = {2019}, abstract = {The propagation of a seismic rupture on a fault introduces spatial variations in the seismic wave field surrounding the fault. This directivity effect results in larger shaking amplitudes in the rupture propagation direction. Its seismic radiation pattern also causes amplitude variations between the strike-normal and strike-parallel components of horizontal ground motion. We investigated the landslide response to these effects during the 2016 Kumamoto earthquake (M-w 7.1) in central Kyushu (Japan). Although the distribution of some 1500 earthquake-triggered landslides as a function of rupture distance is consistent with the observed Arias intensity, the landslides were more concentrated to the northeast of the southwest-northeast striking rupture. We examined several landslide susceptibility factors: hillslope inclination, the median amplification factor (MAF) of ground shaking, lithology, land cover, and topographic wetness. None of these factors sufficiently explains the landslide distribution or orientation (aspect), although the landslide head scarps have an elevated hillslope inclination and MAF. We propose a new physics-based ground-motion model (GMM) that accounts for the seismic rupture effects, and we demonstrate that the low-frequency seismic radiation pattern is consistent with the overall landslide distribution. Its spatial pattern is influenced by the rupture directivity effect, whereas landslide aspect is influenced by amplitude variations between the fault-normal and fault-parallel motion at frequencies < 2 Hz. This azimuth dependence implies that comparable landslide concentrations can occur at different distances from the rupture. This quantitative link between the prevalent landslide aspect and the low-frequency seismic radiation pattern can improve coseismic landslide hazard assessment.}, language = {en} } @article{HaberPohlmeierToetzkeLehmannetal.2019, author = {Haber-Pohlmeier, Sabina and T{\"o}tzke, Christian and Lehmann, E. and Kardjilov, Nikolay and Pohlmeier, A. and Oswald, Sascha}, title = {Combination of magnetic resonance imaging and neutron computed tomography for three-dimensional rhizosphere imaging}, series = {Vadose zone journal}, volume = {18}, journal = {Vadose zone journal}, number = {1}, publisher = {Soil Science Society of America}, address = {Madison}, issn = {1539-1663}, doi = {10.2136/vzj2018.09.0166}, pages = {11}, year = {2019}, abstract = {Core Ideas 3D MRI relaxation time maps reflect water mobility in root, rhizosphere, and soil. 3D NCT water content maps of the same plant complement relaxation time maps. The relaxation time T1 decreases from soil to root, whereas water content increases. Parameters together indicate modification of rhizosphere pore space by gel phase. The zone of reduced T1 corresponds to the zone remaining dry after rewetting. In situ investigations of the rhizosphere require high-resolution imaging techniques, which allow a look into the optically opaque soil compartment. We present the novel combination of magnetic resonance imaging (MRI) and neutron computed tomography (NCT) to achieve synergistic information such as water mobility in terms of three-dimensional (3D) relaxation time maps and total water content maps. Besides a stationary MRI scanner for relaxation time mapping, we used a transportable MRI system on site in the NCT facility to capture rhizosphere properties before desiccation and after subsequent rewetting. First, we addressed two questions using water-filled test capillaries between 0.1 and 5 mm: which root diameters can still be detected by both methods, and to what extent are defined interfaces blurred by these imaging techniques? Going to real root system architecture, we demonstrated the sensitivity of the transportable MRI device by co-registration with NCT and additional validation using X-ray computed tomography. Under saturated conditions, we observed for the rhizosphere in situ a zone with shorter T1 relaxation time across a distance of about 1 mm that was not caused by reduced water content, as proven by successive NCT measurements. We conclude that the effective pore size in the pore network had changed, induced by a gel phase. After rewetting, NCT images showed a dry zone persisting while the MRI intensity inside the root increased considerably, indicating water uptake from the surrounding bulk soil through the still hydrophobic rhizosphere. Overall, combining NCT and MRI allows a more detailed analysis of the rhizosphere's functioning.}, language = {en} } @article{JingHesseKumaretal.2019, author = {Jing, Miao and Hesse, Falk and Kumar, Rohini and Kolditz, Olaf and Kalbacher, Thomas and Attinger, Sabine}, title = {Influence of input and parameter uncertainty on the prediction of catchment-scale groundwater travel time distributions}, series = {Hydrology and earth system sciences : HESS}, volume = {23}, journal = {Hydrology and earth system sciences : HESS}, number = {1}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1027-5606}, doi = {10.5194/hess-23-171-2019}, pages = {171 -- 190}, year = {2019}, abstract = {Groundwater travel time distributions (TTDs) provide a robust description of the subsurface mixing behavior and hydrological response of a subsurface system. Lagrangian particle tracking is often used to derive the groundwater TTDs. The reliability of this approach is subjected to the uncertainty of external forcings, internal hydraulic properties, and the interplay between them. Here, we evaluate the uncertainty of catchment groundwater TTDs in an agricultural catchment using a 3-D groundwater model with an overall focus on revealing the relationship between external forcing, internal hydraulic properties, and TTD predictions. Eight recharge realizations are sampled from a high-resolution dataset of land surface fluxes and states. Calibration-constrained hydraulic conductivity fields (Ks fields) are stochastically generated using the null-space Monte Carlo (NSMC) method for each recharge realization. The random walk particle tracking (RWPT) method is used to track the pathways of particles and compute travel times. Moreover, an analytical model under the random sampling (RS) assumption is fit against the numerical solutions, serving as a reference for the mixing behavior of the model domain. The StorAge Selection (SAS) function is used to interpret the results in terms of quantifying the systematic preference for discharging young/old water. The simulation results reveal the primary effect of recharge on the predicted mean travel time (MTT). The different realizations of calibration-constrained Ks fields moderately magnify or attenuate the predicted MTTs. The analytical model does not properly replicate the numerical solution, and it underestimates the mean travel time. Simulated SAS functions indicate an overall preference for young water for all realizations. The spatial pattern of recharge controls the shape and breadth of simulated TTDs and SAS functions by changing the spatial distribution of particles' pathways. In conclusion, overlooking the spatial nonuniformity and uncertainty of input (forcing) will result in biased travel time predictions. We also highlight the worth of reliable observations in reducing predictive uncertainty and the good interpretability of SAS functions in terms of understanding catchment transport processes.}, language = {en} } @article{RoezerKreibichSchroeteretal.2019, author = {R{\"o}zer, Viktor and Kreibich, Heidi and Schr{\"o}ter, Kai and M{\"u}ller, Meike and Sairam, Nivedita and Doss-Gollin, James and Lall, Upmanu and Merz, Bruno}, title = {Probabilistic Models Significantly Reduce Uncertainty in Hurricane Harvey Pluvial Flood Loss Estimates}, series = {Earths future}, volume = {7}, journal = {Earths future}, number = {4}, publisher = {American Geophysical Union}, address = {Washington}, issn = {2328-4277}, doi = {10.1029/2018EF001074}, pages = {384 -- 394}, year = {2019}, abstract = {Pluvial flood risk is mostly excluded in urban flood risk assessment. However, the risk of pluvial flooding is a growing challenge with a projected increase of extreme rainstorms compounding with an ongoing global urbanization. Considered as a flood type with minimal impacts when rainfall rates exceed the capacity of urban drainage systems, the aftermath of rainfall-triggered flooding during Hurricane Harvey and other events show the urgent need to assess the risk of pluvial flooding. Due to the local extent and small-scale variations, the quantification of pluvial flood risk requires risk assessments on high spatial resolutions. While flood hazard and exposure information is becoming increasingly accurate, the estimation of losses is still a poorly understood component of pluvial flood risk quantification. We use a new probabilistic multivariable modeling approach to estimate pluvial flood losses of individual buildings, explicitly accounting for the associated uncertainties. Except for the water depth as the common most important predictor, we identified the drivers for having loss or not and for the degree of loss to be different. Applying this approach to estimate and validate building structure losses during Hurricane Harvey using a property level data set, we find that the reliability and dispersion of predictive loss distributions vary widely depending on the model and aggregation level of property level loss estimates. Our results show that the use of multivariable zero-inflated beta models reduce the 90\% prediction intervalsfor Hurricane Harvey building structure loss estimates on average by 78\% (totalling U.S.\$3.8 billion) compared to commonly used models.}, language = {en} } @article{SteirouGerlitzApeletal.2019, author = {Steirou, Eva and Gerlitz, Lars and Apel, Heiko and Sun, Xun and Merz, Bruno}, title = {Climate influences on flood probabilities across Europe}, series = {Hydrology and earth system sciences : HESS}, volume = {23}, journal = {Hydrology and earth system sciences : HESS}, number = {3}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1027-5606}, doi = {10.5194/hess-23-1305-2019}, pages = {1305 -- 1322}, year = {2019}, abstract = {The link between streamflow extremes and climatology has been widely studied in recent decades. However, a study investigating the effect of large-scale circulation variations on the distribution of seasonal discharge extremes at the European level is missing. Here we fit a climate-informed generalized extreme value (GEV) distribution to about 600 streamflow records in Europe for each of the standard seasons, i.e., to winter, spring, summer and autumn maxima, and compare it with the classical GEV distribution with parameters invariant in time. The study adopts a Bayesian framework and covers the period 1950 to 2016. Five indices with proven influence on the European climate are examined independently as covariates, namely the North Atlantic Oscillation (NAO), the east Atlantic pattern (EA), the east Atlantic-western Russian pattern (EA/WR), the Scandinavia pattern (SCA) and the polar-Eurasian pattern (POL). It is found that for a high percentage of stations the climate-informed model is preferred to the classical model. Particularly for NAO during winter, a strong influence on streamflow extremes is detected for large parts of Europe (preferred to the classical GEV distribution for 46\% of the stations). Climate-informed fits are characterized by spatial coherence and form patterns that resemble relations between the climate indices and seasonal precipitation, suggesting a prominent role of the considered circulation modes for flood generation. For certain regions, such as northwestern Scandinavia and the British Isles, yearly variations of the mean seasonal climate indices result in considerably different extreme value distributions and thus in highly different flood estimates for individual years that can also persist for longer time periods.}, language = {en} } @article{SairamSchroeterLuedtkeetal.2019, author = {Sairam, Nivedita and Schr{\"o}ter, Kai and L{\"u}dtke, Stefan and Merz, Bruno and Kreibich, Heidi}, title = {Quantifying Flood Vulnerability Reduction via Private Precaution}, series = {Earth future}, volume = {7}, journal = {Earth future}, number = {3}, publisher = {American Geophysical Union}, address = {Washington}, issn = {2328-4277}, doi = {10.1029/2018EF000994}, pages = {235 -- 249}, year = {2019}, abstract = {Private precaution is an important component in contemporary flood risk management and climate adaptation. However, quantitative knowledge about vulnerability reduction via private precautionary measures is scarce and their effects are hardly considered in loss modeling and risk assessments. However, this is a prerequisite to enable temporally dynamic flood damage and risk modeling, and thus the evaluation of risk management and adaptation strategies. To quantify the average reduction in vulnerability of residential buildings via private precaution empirical vulnerability data (n = 948) is used. Households with and without precautionary measures undertaken before the flood event are classified into treatment and nontreatment groups and matched. Postmatching regression is used to quantify the treatment effect. Additionally, we test state-of-the-art flood loss models regarding their capability to capture this difference in vulnerability. The estimated average treatment effect of implementing private precaution is between 11 and 15 thousand EUR per household, confirming the significant effectiveness of private precautionary measures in reducing flood vulnerability. From all tested flood loss models, the expert Bayesian network-based model BN-FLEMOps and the rule-based loss model FLEMOps perform best in capturing the difference in vulnerability due to private precaution. Thus, the use of such loss models is suggested for flood risk assessments to effectively support evaluations and decision making for adaptable flood risk management.}, language = {en} } @article{HerzschuhCaoLaeppleetal.2019, author = {Herzschuh, Ulrike and Cao, Xianyong and Laepple, Thomas and Dallmeyer, Anne and Telford, Richard J. and Ni, Jian and Chen, Fahu and Kong, Zhaochen and Liu, Guangxiu and Liu, Kam-Biu and Liu, Xingqi and Stebich, Martina and Tang, Lingyu and Tian, Fang and Wang, Yongbo and Wischnewski, Juliane and Xu, Qinghai and Yan, Shun and Yang, Zhenjing and Yu, Ge and Zhang, Yun and Zhao, Yan and Zheng, Zhuo}, title = {Position and orientation of the westerly jet determined Holocene rainfall patterns in China}, series = {Nature Communications}, volume = {10}, journal = {Nature Communications}, publisher = {Nature Publ. Group}, address = {London}, issn = {2041-1723}, doi = {10.1038/s41467-019-09866-8}, pages = {8}, year = {2019}, abstract = {Proxy-based reconstructions and modeling of Holocene spatiotemporal precipitation patterns for China and Mongolia have hitherto yielded contradictory results indicating that the basic mechanisms behind the East Asian Summer Monsoon and its interaction with the westerly jet stream remain poorly understood. We present quantitative reconstructions of Holocene precipitation derived from 101 fossil pollen records and analyse them with the help of a minimal empirical model. We show that the westerly jet-stream axis shifted gradually southward and became less tilted since the middle Holocene. This was tracked by the summer monsoon rain band resulting in an early-Holocene precipitation maximum over most of western China, a mid-Holocene maximum in north-central and northeastern China, and a late-Holocene maximum in southeastern China. Our results suggest that a correct simulation of the orientation and position of the westerly jet stream is crucial to the reliable prediction of precipitation patterns in China and Mongolia.}, language = {en} } @article{CostaTomazdeSouzaAyzelHeistermann2020, author = {Costa Tomaz de Souza, Arthur and Ayzel, Georgy and Heistermann, Maik}, title = {Quantifying the location error of precipitation nowcasts}, series = {Advances in meteorology}, volume = {2020}, journal = {Advances in meteorology}, publisher = {Hindawi}, address = {London}, issn = {1687-9309}, doi = {10.1155/2020/8841913}, pages = {12}, year = {2020}, abstract = {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.}, language = {en} } @article{OguntundeAbiodunLischeidetal.2020, author = {Oguntunde, Philip G. and Abiodun, Babatunde Joseph and Lischeid, Gunnar and Abatan, Abayomi A.}, title = {Droughts projection over the Niger and Volta River basins of West Africa at specific global warming levels}, series = {International Journal of Climatology}, volume = {40}, journal = {International Journal of Climatology}, number = {13}, publisher = {John Wiley \& Sons, Inc.}, address = {New Jersey}, pages = {12}, year = {2020}, abstract = {This study investigates possible impacts of four global warming levels (GWLs: GWL1.5, GWL2.0, GWL2.5, and GWL3.0) on drought characteristics over Niger River basin (NRB) and Volta River basin (VRB). Two drought indices-Standardized Precipitation Index (SPI) and Standardized Precipitation-Evapotranspiration Index (SPEI)-were employed in characterizing droughts in 20 multi-model simulation outputs from the Coordinated Regional Climate Downscaling Experiment (CORDEX). The performance of the simulation in reproducing basic hydro-climatological features and severe drought characteristics (i.e., magnitude and frequency) in the basins were evaluated. The projected changes in the future drought frequency were quantified and compared under the four GWLs for two climate forcing scenarios (RCP8.5 and RCP4.5). The regional climate model (RCM) ensemble gives a realistic simulation of historical hydro-climatological variables needed to calculate the drought indices. With SPEI, the simulation ensemble projects an increase in the magnitude and frequency of severe droughts over both basins (NRB and VRB) at all GWLs, but the increase, which grows with the GWLs, is higher over NRB than over VRB. More than 75\% of the simulations agree on the projected increase at GWL1.5 and all simulations agree on the increase at higher GWLs. With SPI, the projected changes in severe drought is weaker and the magnitude remains the same at all GWLs, suggesting that SPI projection may underestimate impacts of the GWLs on the intensity and severity of future drought. The results of this study have application in mitigating impact of global warming on future drought risk over the regional water systems.}, language = {en} }