@article{BrillPassuniPinedaEspichanCuyaetal.2020,
  author    = {Brill, Fabio Alexander and Passuni Pineda, Silvia and Espichan Cuya, Bruno and Kreibich, Heidi},
  title     = {A data-mining approach towards damage modelling for El Nino events in Peru},
  series = {Geomatics, natural hazards and risk},
  volume    = {11},
  journal   = {Geomatics, natural hazards and risk},
  number    = {1},
  publisher = {Routledge, Taylor \& Francis Group},
  address   = {Abingdon},
  issn      = {1947-5705},
  doi       = {10.1080/19475705.2020.1818636},
  pages     = {1966 -- 1990},
  year      = {2020},
  abstract  = {Compound natural hazards likeEl Ninoevents cause high damage to society, which to manage requires reliable risk assessments. Damage modelling is a prerequisite for quantitative risk estimations, yet many procedures still rely on expert knowledge, and empirical studies investigating damage from compound natural hazards hardly exist. A nationwide building survey in Peru after theEl Ninoevent 2017 - which caused intense rainfall, ponding water, flash floods and landslides - enables us to apply data-mining methods for statistical groundwork, using explanatory features generated from remote sensing products and open data. We separate regions of different dominant characteristics through unsupervised clustering, and investigate feature importance rankings for classifying damage via supervised machine learning. Besides the expected effect of precipitation, the classification algorithms select the topographic wetness index as most important feature, especially in low elevation areas. The slope length and steepness factor ranks high for mountains and canyons. Partial dependence plots further hint at amplified vulnerability in rural areas. An example of an empirical damage probability map, developed with a random forest model, is provided to demonstrate the technical feasibility.},
  language  = {en}
}
@article{SamprognaMohorHudsonThieken2020,
  author    = {Samprogna Mohor, Guilherme and Hudson, Paul and Thieken, Annegret},
  title     = {A comparison of factors driving flood losses in households affected by different flood types},
  series = {Water resources research},
  volume    = {56},
  journal   = {Water resources research},
  number    = {4},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0043-1397},
  doi       = {10.1029/2019WR025943},
  pages     = {20},
  year      = {2020},
  abstract  = {Flood loss data collection and modeling are not standardized, and previous work has indicated that losses from different flood types (e.g., riverine and groundwater) may follow different driving forces. However, different flood types may occur within a single flood event, which is known as a compound flood event. Therefore, we aimed to identify statistical similarities between loss-driving factors across flood types and test whether the corresponding losses should be modeled separately. In this study, we used empirical data from 4,418 respondents from four survey campaigns studying households in Germany that experienced flooding. These surveys sought to investigate several features of the impact process (hazard, socioeconomic, preparedness, and building characteristics, as well as flood type). While the level of most of these features differed across flood type subsamples (e.g., degree of preparedness), they did so in a nonregular pattern. A variable selection process indicates that besides hazard and building characteristics, information on property-level preparedness was also selected as a relevant predictor of the loss ratio. These variables represent information, which is rarely adopted in loss modeling. Models shall be refined with further data collection and other statistical methods. To save costs, data collection efforts should be steered toward the most relevant predictors to enhance data availability and increase the statistical power of results. Understanding that losses from different flood types are driven by different factors is a crucial step toward targeted data collection and model development and will finally clarify conditions that allow us to transfer loss models in space and time. <br /> Key Points <br /> Survey data of flood-affected households show different concurrent flood types, undermining the use of a single-flood-type loss model Thirteen variables addressing flood hazard, the building, and property level preparedness are significant predictors of the building loss ratio Flood type-specific models show varying significance across the predictor variables, indicating a hindrance to model transferability},
  language  = {en}
}
@article{KrmičekTimmermanZiemannetal.2020,
  author    = {Krm{\´i}ček, Luk{\´a}š and Timmerman, Martin Jan and Ziemann, Martin Andreas and Sudo, Masafumi and Ulrych, Jaromir},
  title     = {40Ar/39Ar step-heating dating of phlogopite and kaersutite megacrysts from the Železn{\´a} hůrka (Eisenb{\"u}hl) Pleistocene scoria cone, Czech Republic},
  series = {Geologica Carpathica},
  volume    = {71},
  journal   = {Geologica Carpathica},
  number    = {4},
  publisher = {Veda},
  address   = {Bratislava},
  issn      = {1335-0552},
  doi       = {10.31577/GeolCarp.71.4.6},
  pages     = {382 -- 387},
  year      = {2020},
  abstract  = {(40)A/Ar-39 step-heating of mica and amphibole megacrysts from hauyne-bearing olivine melilitite scoria/tephra from the Zelezna hurka yielded a 435 +/- 108 ka isotope correlation age for phlogopite and a more imprecise 1.55 Ma total gas age of the kaersutite megacryst. The amphibole megacrysts may constitute the first, and the younger phlogopite megacrysts the later phase of mafic, hydrous melilitic magma crystallization. It cannot be ruled out that the amphibole megacrysts are petrogenetically unrelated to tephra and phlogopite megacrysts and were derived from mantle xenoliths or disaggregated older, deep crustal pegmatites. This is in line both with the rarity of amphibole at Zelezna hurka and with the observed signs of magmatic resorption at the edges of amphibole crystals.},
  language  = {en}
}
@article{KoyanTronicke2020,
  author    = {Koyan, Philipp and Tronicke, Jens},
  title     = {3D modeling of ground-penetrating radar data across a realistic sedimentary model},
  series = {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},
  volume    = {137},
  journal   = {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},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0098-3004},
  doi       = {10.1016/j.cageo.2020.104422},
  pages     = {9},
  year      = {2020},
  abstract  = {Ground-penetrating radar (GPR) is an established geophysical tool to explore a wide range of near-surface environments. Today, the use of synthetic GPR data is largely limited to 2D because 3D modeling is computationally more expensive. In fact, only recent developments of modeling tools and powerful hardware allow for a time-efficient computation of extensive 3D data sets. Thus, 3D subsurface models and resulting GPR data sets, which are of great interest to develop and evaluate novel approaches in data analysis and interpretation, have not been made publicly available up to now. <br /> We use a published hydrofacies data set of an aquifer-analog study within fluvio-glacial deposits to infer a realistic 3D porosity model showing heterogeneities at multiple spatial scales. Assuming fresh-water saturated sediments, we generate synthetic 3D GPR data across this model using novel GPU-acceleration included in the open-source software gprMax. We present a numerical approach to examine 3D wave-propagation effects in modeled GPR data. Using the results of this examination study, we conduct a spatial model decomposition to enable a computationally efficient 3D simulation of a typical GPR reflection data set across the entire model surface. We process the resulting GPR data set using a standard 3D structural imaging sequence and compare the results to selected input data to demonstrate the feasibility and potential of the presented modeling studies. We conclude on conceivable applications of our 3D GPR reflection data set and the underlying porosity model, which are both publicly available and, thus, can support future methodological developments in GPR and other near-surface geophysical techniques.},
  language  = {en}
}