@article{SairamBrillSiegetal.2021, author = {Sairam, Nivedita and Brill, Fabio Alexander and Sieg, Tobias and Farrag, Mostafa and Kellermann, Patric and Viet Dung Nguyen, and L{\"u}dtke, Stefan and Merz, Bruno and Schr{\"o}ter, Kai and Vorogushyn, Sergiy and Kreibich, Heidi}, title = {Process-based flood risk assessment for Germany}, series = {Earth's future / American Geophysical Union}, volume = {9}, journal = {Earth's future / American Geophysical Union}, number = {10}, publisher = {Wiley-Blackwell}, address = {Hoboken, NJ}, issn = {2328-4277}, doi = {10.1029/2021EF002259}, pages = {12}, year = {2021}, abstract = {Large-scale flood risk assessments are crucial for decision making, especially with respect to new flood defense schemes, adaptation planning and estimating insurance premiums. We apply the process-based Regional Flood Model (RFM) to simulate a 5000-year flood event catalog for all major catchments in Germany and derive risk curves based on the losses per economic sector. The RFM uses a continuous process simulation including a multisite, multivariate weather generator, a hydrological model considering heterogeneous catchment processes, a coupled 1D-2D hydrodynamic model considering dike overtopping and hinterland storage, spatially explicit sector-wise exposure data and empirical multi-variable loss models calibrated for Germany. For all components, uncertainties in the data and models are estimated. We estimate the median Expected Annual Damage (EAD) and Value at Risk at 99.5\% confidence for Germany to be euro0.529 bn and euro8.865 bn, respectively. The commercial sector dominates by making about 60\% of the total risk, followed by the residential sector. The agriculture sector gets affected by small return period floods and only contributes to less than 3\% to the total risk. The overall EAD is comparable to other large-scale estimates. However, the estimation of losses for specific return periods is substantially improved. The spatial consistency of the risk estimates avoids the large overestimation of losses for rare events that is common in other large-scale assessments with homogeneous return periods. Thus, the process-based, spatially consistent flood risk assessment by RFM is an important step forward and will serve as a benchmark for future German-wide flood risk assessments.}, 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{KayatzBaroniHillieretal.2018, author = {Kayatz, Benjamin and Baroni, Gabriele and Hillier, Jon and L{\"u}dtke, Stefan and Heathcote, Richard and Malin, Daniella and van Tonder, Carl and Kuster, Benjamin and Freese, Dirk and H{\"u}ttl, Reinhard and Wattenbach, Martin}, title = {Cool farm tool water}, series = {Journal of cleaner production}, volume = {207}, journal = {Journal of cleaner production}, publisher = {Elsevier}, address = {Oxford}, issn = {0959-6526}, pages = {1163 -- 1179}, year = {2018}, abstract = {The agricultural sector accounts for 70\% of all water consumption and poses great pressure on ground water resources. Therefore, evaluating agricultural water consumption is highly important as it allows supply chain actors to identify practices which are associated with unsustainable water use, which risk depleting current water resources and impacting future production. However, these assessments are often not feasible for crop producers as data, models and experiments are required in order to conduct them. This work introduces a new on-line agricultural water use assessment tool that provides the water footprint and irrigation requirements at field scale based on an enhanced FAO56 approach combined with a global climate, crop and soil databases. This has been included in the Cool Farm Tool - an online tool which already provides metrics for greenhouse gas emissions and biodiversity impacts and therefore allows for a more holistic assessment of environmental sustainability in farming and agricultural supply chains. The model is tested against field scale and state level water footprint data providing good results. The tool provides a practical, reliable way to assess agricultural water use, and offers a means to engage growers and stakeholders in identifying efficient water management practices. (C) 2018 The Authors. Published by Elsevier Ltd.}, language = {en} } @article{NiedSchroeterLuedtkeetal.2017, author = {Nied, Manuela and Schr{\"o}ter, Kai and L{\"u}dtke, Stefan and Nguyen, Viet Dung and Merz, Bruno}, title = {What are the hydro-meteorological controls on flood characteristics?}, series = {Journal of hydrology}, volume = {545}, journal = {Journal of hydrology}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0022-1694}, doi = {10.1016/j.jhydrol.2016.12.003}, pages = {310 -- 326}, year = {2017}, abstract = {Flood events can be expressed by a variety of characteristics such as flood magnitude and extent, event duration or incurred loss. Flood estimation and management may benefit from understanding how the different flood characteristics relate to the hydrological catchment conditions preceding the event and to the meteorological conditions throughout the event. In this study, we therefore propose a methodology to investigate the hydro-meteorological controls on different flood characteristics, based on the simulation of the complete flood risk chain from the flood triggering precipitation event, through runoff generation in the catchment, flood routing and possible inundation in the river system and floodplains to flood loss. Conditional cumulative distribution functions and regression tree analysis delineate the seasonal varying flood processes and indicate that the effect of the hydrological pre-conditions, i.e. soil moisture patterns, and of the meteorological conditions, i.e. weather patterns, depends on the considered flood characteristic. The methodology is exemplified for the Elbe catchment. In this catchment, the length of the build-up period, the event duration and the number of gauges undergoing at least a 10-year flood are governed by weather patterns. The affected length and the number of gauges undergoing at least a 2-year flood are however governed by soil moisture patterns. In case of flood severity and loss, the controlling factor is less pronounced. Severity is slightly governed by soil moisture patterns whereas loss is slightly governed by weather patterns. The study highlights that flood magnitude and extent arise from different flood generation processes and concludes that soil moisture patterns as well as weather patterns are not only beneficial to inform on possible flood occurrence but also on the involved flood processes and resulting flood characteristics.}, language = {en} }