@article{SchoppaSiegVogeletal.2020, author = {Schoppa, Lukas and Sieg, Tobias and Vogel, Kristin and Z{\"o}ller, Gert and Kreibich, Heidi}, title = {Probabilistic flood loss models for companies}, series = {Water resources research}, volume = {56}, journal = {Water resources research}, number = {9}, publisher = {American Geophysical Union}, address = {Washington}, issn = {0043-1397}, doi = {10.1029/2020WR027649}, pages = {19}, year = {2020}, abstract = {Flood loss modeling is a central component of flood risk analysis. Conventionally, this involves univariable and deterministic stage-damage functions. Recent advancements in the field promote the use of multivariable and probabilistic loss models, which consider variables beyond inundation depth and account for prediction uncertainty. Although companies contribute significantly to total loss figures, novel modeling approaches for companies are lacking. Scarce data and the heterogeneity among companies impede the development of company flood loss models. We present three multivariable flood loss models for companies from the manufacturing, commercial, financial, and service sector that intrinsically quantify prediction uncertainty. Based on object-level loss data (n = 1,306), we comparatively evaluate the predictive capacity of Bayesian networks, Bayesian regression, and random forest in relation to deterministic and probabilistic stage-damage functions, serving as benchmarks. The company loss data stem from four postevent surveys in Germany between 2002 and 2013 and include information on flood intensity, company characteristics, emergency response, private precaution, and resulting loss to building, equipment, and goods and stock. We find that the multivariable probabilistic models successfully identify and reproduce essential relationships of flood damage processes in the data. The assessment of model skill focuses on the precision of the probabilistic predictions and reveals that the candidate models outperform the stage-damage functions, while differences among the proposed models are negligible. Although the combination of multivariable and probabilistic loss estimation improves predictive accuracy over the entire data set, wide predictive distributions stress the necessity for the quantification of uncertainty.}, language = {en} } @article{LaudanRoezerSiegetal.2017, author = {Laudan, Jonas and Roezer, Viktor and Sieg, Tobias and Vogel, Kristin and Thieken, Annegret}, title = {Damage assessment in Braunsbach 2016: data collection and analysis for an improved understanding of damaging processes during flash floods}, series = {Natural hazards and earth system sciences}, volume = {17}, journal = {Natural hazards and earth system sciences}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1561-8633}, doi = {10.5194/nhess-17-2163-2017}, pages = {2163 -- 2179}, year = {2017}, language = {en} } @article{VogelOzturkRiemeretal.2017, author = {Vogel, Kristin and Ozturk, Ugur and Riemer, Adrian and Laudan, Jonas and Sieg, Tobias and Wendi, Dadiyorto and Agarwal, Ankit and Roezer, Viktor and Korup, Oliver and Thieken, Annegret}, title = {Die Sturzflut von Braunsbach am 29. Mai 2016 - Entstehung, Ablauf und Sch{\"a}den eines „Jahrhundertereignisses"}, series = {Hydrologie und Wasserbewirtschaftung}, volume = {61}, journal = {Hydrologie und Wasserbewirtschaftung}, number = {3}, publisher = {Bundesanst. f{\"u}r Gew{\"a}sserkunde}, address = {Koblenz}, issn = {1439-1783}, doi = {10.5675/HyWa_2017,3_2}, pages = {163 -- 175}, year = {2017}, abstract = {Am Abend des 29. Mai 2016 wurde der Ort Braunsbach im Landkreis Schw{\"a}bisch-Hall (Baden-W{\"u}rttemberg) von einer Sturzflut getroffen, bei der mehrere H{\"a}user stark besch{\"a}digt oder zerst{\"o}rt wurden. Die Sturzflut war eine der Unwetterfolgen, die im Fr{\"u}hsommer 2016 vom Tiefdruckgebiet Elvira ausgel{\"o}st wurden. Der vorliegende Bericht ist der zweite Teil einer Doppelver{\"o}ffentlichung, welche die Ergebnisse zur Untersuchung des Sturzflutereignisses im Rahmen des DFG-Graduiertenkollegs "Naturgefahren und Risiken in einer sich ver{\"a}ndernden Welt" (NatRiskChange, GRK 2043/1) der Universit{\"a}t Potsdam pr{\"a}sentiert. W{\"a}hrend Teil 1 die meteorologischen und hydrologischen Ereignisse analysiert, fokussiert Teil 2 auf die geomorphologischen Prozesse und die verursachten Geb{\"a}udesch{\"a}den. Dazu wurden Ursprung und Ausmaß des w{\"a}hrend des Sturzflutereignisses mobilisierten und in den Ort getragenen Materials untersucht. Des Weiteren wurden zu 96 betroffenen Geb{\"a}uden Daten zum Schadensgrad sowie Prozess- und Geb{\"a}udecharakteristika aufgenommen und ausgewertet. Die Untersuchungen zeigen, dass bei der Betrachtung von Hochwassergef{\"a}hrdung die Ber{\"u}cksichtigung von Sturzfluten und ihrer speziellen Charakteristika, wie hoher Feststofftransport und sprunghaftes Verhalten insbesondere in bebautem Gel{\"a}nde, wesentlich ist, um effektive Schutzmaßnahmen ergreifen zu k{\"o}nnen.}, language = {de} } @article{SiegVogelMerzetal.2017, author = {Sieg, Tobias and Vogel, Kristin and Merz, Bruno and Kreibich, Heidi}, title = {Tree-based flood damage modeling of companies: Damage processes and model performance}, series = {Water resources research}, volume = {53}, journal = {Water resources research}, publisher = {American Geophysical Union}, address = {Washington}, issn = {0043-1397}, doi = {10.1002/2017WR020784}, pages = {6050 -- 6068}, year = {2017}, abstract = {Reliable flood risk analyses, including the estimation of damage, are an important prerequisite for efficient risk management. However, not much is known about flood damage processes affecting companies. Thus, we conduct a flood damage assessment of companies in Germany with regard to two aspects. First, we identify relevant damage-influencing variables. Second, we assess the prediction performance of the developed damage models with respect to the gain by using an increasing amount of training data and a sector-specific evaluation of the data. Random forests are trained with data from two postevent surveys after flood events occurring in the years 2002 and 2013. For a sector-specific consideration, the data set is split into four subsets corresponding to the manufacturing, commercial, financial, and service sectors. Further, separate models are derived for three different company assets: buildings, equipment, and goods and stock. Calculated variable importance values reveal different variable sets relevant for the damage estimation, indicating significant differences in the damage process for various company sectors and assets. With an increasing number of data used to build the models, prediction errors decrease. Yet the effect is rather small and seems to saturate for a data set size of several hundred observations. In contrast, the prediction improvement achieved by a sector-specific consideration is more distinct, especially for damage to equipment and goods and stock. Consequently, sector-specific data acquisition and a consideration of sector-specific company characteristics in future flood damage assessments is expected to improve the model performance more than a mere increase in data.}, language = {en} } @article{SiegSchinkoVogeletal.2019, author = {Sieg, Tobias and Schinko, Thomas and Vogel, Kristin and Mechler, Reinhard and Merz, Bruno and Kreibich, Heidi}, title = {Integrated assessment of short-term direct and indirect economic flood impacts including uncertainty quantification}, series = {PLoS ONE}, volume = {14}, journal = {PLoS ONE}, number = {4}, publisher = {Public Library of Science}, address = {San Francisco}, issn = {1932-6203}, doi = {10.1371/journal.pone.0212932}, pages = {21}, year = {2019}, abstract = {Understanding and quantifying total economic impacts of flood events is essential for flood risk management and adaptation planning. Yet, detailed estimations of joint direct and indirect flood-induced economic impacts are rare. In this study an innovative modeling procedure for the joint assessment of short-term direct and indirect economic flood impacts is introduced. The procedure is applied to 19 economic sectors in eight federal states of Germany after the flood events in 2013. The assessment of the direct economic impacts is object-based and considers uncertainties associated with the hazard, the exposed objects and their vulnerability. The direct economic impacts are then coupled to a supply-side Input-Output-Model to estimate the indirect economic impacts. The procedure provides distributions of direct and indirect economic impacts which capture the associated uncertainties. The distributions of the direct economic impacts in the federal states are plausible when compared to reported values. The ratio between indirect and direct economic impacts shows that the sectors Manufacturing, Financial and Insurance activities suffered the most from indirect economic impacts. These ratios also indicate that indirect economic impacts can be almost as high as direct economic impacts. They differ strongly between the economic sectors indicating that the application of a single factor as a proxy for the indirect impacts of all economic sectors is not appropriate.}, language = {en} } @article{SiegVogelMerzetal.2019, author = {Sieg, Tobias and Vogel, Kristin and Merz, Bruno and Kreibich, Heidi}, title = {Seamless Estimation of Hydrometeorological Risk Across Spatial Scales}, series = {Earth's Future}, volume = {7}, journal = {Earth's Future}, number = {5}, publisher = {Wiley-Blackwell}, address = {Hoboken, NJ}, issn = {2328-4277}, doi = {10.1029/2018EF001122}, pages = {574 -- 581}, year = {2019}, abstract = {Hydrometeorological hazards caused losses of approximately 110 billion U.S. Dollars in 2016 worldwide. Current damage estimations do not consider the uncertainties in a comprehensive way, and they are not consistent between spatial scales. Aggregated land use data are used at larger spatial scales, although detailed exposure data at the object level, such as openstreetmap.org, is becoming increasingly available across the globe.We present a probabilistic approach for object-based damage estimation which represents uncertainties and is fully scalable in space. The approach is applied and validated to company damage from the flood of 2013 in Germany. Damage estimates are more accurate compared to damage models using land use data, and the estimation works reliably at all spatial scales. Therefore, it can as well be used for pre-event analysis and risk assessments. This method takes hydrometeorological damage estimation and risk assessments to the next level, making damage estimates and their uncertainties fully scalable in space, from object to country level, and enabling the exploitation of new exposure data.}, language = {en} }