TY - JOUR A1 - Taffarello, Denise A1 - Srinivasan, Raghavan A1 - Samprogna Mohor, Guilherme A1 - Bittencourt Guimaraes, Joao Luis A1 - Calijuri, Maria do Carmo A1 - Mendiondo, Eduardo Mario T1 - Modeling freshwater quality scenarios with ecosystem-based adaptation in the headwaters of the Cantareira system, Brazil JF - Hydrology and earth system sciences : HESS N2 - Although hydrologic models provide hypothesis testing of complex dynamics occurring at catchments, fresh-water quality modeling is still incipient at many subtropical headwaters. In Brazil, a few modeling studies assess freshwater nutrients, limiting policies on hydrologic ecosystem services. This paper aims to compare freshwater quality scenarios under different land-use and land-cover (LULC) change, one of them related to ecosystem-based adaptation (EbA), in Brazilian headwaters. Using the spatially semi-distributed Soil and Water Assessment Tool (SWAT) model, nitrate, total phosphorous (TP) and sediment were modeled in catchments ranging from 7.2 to 1037 km(2). These head-waters were eligible areas of the Brazilian payment for ecosystem services (PES) projects in the Cantareira water supply system, which had supplied water to 9 million people in the Sao Paulo metropolitan region (SPMR). We considered SWAT modeling of three LULC scenarios: (i) recent past scenario (S1), with historical LULC in 1990; (ii) current land-use scenario (S2), with LULC for the period 2010-2015 with field validation; and (iii) future land-use scenario with PES (S2 + EbA). This latter scenario proposed forest cover restoration through EbA following the river basin plan by 2035. These three LULC scenarios were tested with a selected record of rainfall and evapotranspiration observed in 2006-2014, with the occurrence of extreme droughts. To assess hydrologic services, we proposed the hydrologic service index (HSI), as a new composite metric comparing water pollution levels (WPL) for reference catchments, related to the grey water footprint (greyWF) and water yield. On the one hand, water quality simulations allowed for the regionalization of greyWF at spatial scales under LULC scenarios. According to the critical threshold, HSI identified areas as less or more sustainable catchments. On the other hand, conservation practices simulated through the S2 + EbA scenario envisaged not only additional and viable best management practices (BMP), but also preventive decision-making at the headwaters of water supply systems. Y1 - 2018 U6 - https://doi.org/10.5194/hess-22-4699-2018 SN - 1027-5606 SN - 1607-7938 VL - 22 IS - 9 SP - 4699 EP - 4723 PB - Copernicus CY - Göttingen ER - TY - GEN A1 - Taffarello, Denise A1 - Srinivasan, Raghavan A1 - Samprogna Mohor, Guilherme A1 - Guimarães, João Luis Bittencourt A1 - Calijuri, Maria do Carmo A1 - Mendiondo, Eduardo Mario T1 - Modeling freshwater quality scenarios with ecosystem-basedadaptation in the headwaters of the Cantareira system, Brazil T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Although hydrologic models provide hypothesis testing of complex dynamics occurring at catchments, fresh-water quality modeling is still incipient at many subtropical headwaters. In Brazil, a few modeling studies assess freshwater nutrients, limiting policies on hydrologic ecosystem services. This paper aims to compare freshwater quality scenarios under different land-use and land-cover (LULC) change, one of them related to ecosystem-based adaptation (EbA), in Brazilian headwaters. Using the spatially semi-distributed Soil and Water Assessment Tool (SWAT) model, nitrate, total phosphorous (TP) and sediment were modeled in catchments ranging from 7.2 to 1037 km(2). These head-waters were eligible areas of the Brazilian payment for ecosystem services (PES) projects in the Cantareira water supply system, which had supplied water to 9 million people in the Sao Paulo metropolitan region (SPMR). We considered SWAT modeling of three LULC scenarios: (i) recent past scenario (S1), with historical LULC in 1990; (ii) current land-use scenario (S2), with LULC for the period 2010-2015 with field validation; and (iii) future land-use scenario with PES (S2 + EbA). This latter scenario proposed forest cover restoration through EbA following the river basin plan by 2035. These three LULC scenarios were tested with a selected record of rainfall and evapotranspiration observed in 2006-2014, with the occurrence of extreme droughts. To assess hydrologic services, we proposed the hydrologic service index (HSI), as a new composite metric comparing water pollution levels (WPL) for reference catchments, related to the grey water footprint (greyWF) and water yield. On the one hand, water quality simulations allowed for the regionalization of greyWF at spatial scales under LULC scenarios. According to the critical threshold, HSI identified areas as less or more sustainable catchments. On the other hand, conservation practices simulated through the S2 + EbA scenario envisaged not only additional and viable best management practices (BMP), but also preventive decision-making at the headwaters of water supply systems. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 935 KW - assessment tool swat KW - international trade KW - atlantic forest KW - soil KW - management KW - services KW - drought KW - trends KW - calibration KW - catchments Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-459253 SN - 1866-8372 IS - 935 SP - 4699 EP - 4723 ER - TY - JOUR A1 - Guzman, Diego A. A1 - Samprogna Mohor, Guilherme A1 - Mendiondo, Eduardo Mario T1 - Multi-year index-based insurance for adapting Water Utility Companies to hydrological drought BT - case study of a water supply system of the Sao Paulo metropolitan region, Brazil JF - Water N2 - The sustainability of water utility companies is threatened by non-stationary drivers, such as climate and anthropogenic changes. To cope with potential economic losses, instruments such as insurance are useful for planning scenarios and mitigating impacts, but data limitations and risk uncertainties affect premium estimation and, consequently, business sustainability. This research estimated the possible economic impacts of business interruption to the Sao Paulo Water Utility Company derived from hydrological drought and how this could be mitigated with an insurance scheme. Multi-year insurance (MYI) was proposed through a set of "change" drivers: the climate driver, through forcing the water evaluation and planning system (WEAP) hydrological tool; the anthropogenic driver, through water demand projections; and the economic driver, associated with recent water price policies adopted by the utility company during water scarcity periods. In our study case, the evaluated indices showed that MYI contracts that cover only longer droughts, regardless of the magnitude, offer better financial performance than contracts that cover all events (in terms of drought duration). Moreover, through MYI contracts, we demonstrate solvency for the insurance fund in the long term and an annual average actuarially fair premium close to the total expected revenue reduction. KW - multi-year insurance KW - climate change KW - hydrological drought KW - water KW - security and economy Y1 - 2020 U6 - https://doi.org/10.3390/w12112954 SN - 2073-4441 VL - 12 IS - 11 PB - MDPI CY - Basel ER - TY - JOUR A1 - Deusdará-Leal, Karinne A1 - Samprogna Mohor, Guilherme A1 - Cuartas, Luz Adriana A1 - Seluchi, Marcelo E. A1 - Marengo, Jose A. A1 - Zhang, Rong A1 - Broedel, Elisangela A1 - Amore, Diogo de Jesus A1 - Alvalá, Regina C. S. A1 - Cunha, Ana Paula M. A. A1 - Gonçalves, José A. C. T1 - Trends and climate elasticity of streamflow in south-eastern Brazil basins JF - Water N2 - Trends in streamflow, rainfall and potential evapotranspiration (PET) time series, from 1970 to 2017, were assessed for five important hydrological basins in Southeastern Brazil. The concept of elasticity was also used to assess the streamflow sensitivity to changes in climate variables, for annual data and 5-, 10- and 20-year moving averages. Significant negative trends in streamflow and rainfall and significant increasing trend in PET were detected. For annual analysis, elasticity revealed that 1% decrease in rainfall resulted in 1.21-2.19% decrease in streamflow, while 1% increase in PET induced different reductions percentages in streamflow, ranging from 2.45% to 9.67%. When both PET and rainfall were computed to calculate the elasticity, results were positive for some basins. Elasticity analysis considering 20-year moving averages revealed that impacts on the streamflow were cumulative: 1% decrease in rainfall resulted in 1.83-4.75% decrease in streamflow, while 1% increase in PET induced 3.47-28.3% decrease in streamflow. This different temporal response may be associated with the hydrological memory of the basins. Streamflow appears to be more sensitive in less rainy basins. This study provides useful information to support strategic government decisions, especially when the security of water resources and drought mitigation are considered in face of climate change. KW - runoff KW - precipitation KW - potential evapotranspiration KW - Pettitt test KW - sensitivity Y1 - 2022 U6 - https://doi.org/10.3390/w14142245 SN - 2073-4441 VL - 14 IS - 14 PB - MDPI CY - Basel ER - TY - JOUR A1 - Thieken, Annegret A1 - Mohor, Guilherme Samprogna A1 - Kreibich, Heidi A1 - Müller, Meike T1 - Compound inland flood events BT - different pathways, different impacts and different coping options JF - Natural hazards and earth system sciences : NHESS N2 - Several severe flood events hit Germany in recent years, with events in 2013 and 2016 being the most destructive ones, although dynamics and flood processes were very different. While the 2013 event was a slowly rising widespread fluvial flood accompanied by some severe dike breaches, the events in 2016 were fast-onset pluvial floods, which resulted in surface water flooding in some places due to limited capacities of the drainage systems and in destructive flash floods with high sediment loads and clogging in others, particularly in small steep catchments. Hence, different pathways, i.e. different routes that the water takes to reach (and potentially damage) receptors, in our case private households, can be identified in both events. They can thus be regarded as spatially compound flood events or compound inland floods. This paper analyses how differently affected residents coped with these different flood types (fluvial and pluvial) and their impacts while accounting for the different pathways (river flood, dike breach, surface water flooding and flash flood) within the compound events. The analyses are based on two data sets with 1652 (for the 2013 flood) and 601 (for the 2016 flood) affected residents who were surveyed around 9 months after each flood, revealing little socio-economic differences - except for income - between the two samples. The four pathways showed significant differences with regard to their hydraulic and financial impacts, recovery, warning processes, and coping and adaptive behaviour. There are just small differences with regard to perceived self-efficacy and responsibility, offering entry points for tailored risk communication and support to improve property-level adaptation. Y1 - 2022 U6 - https://doi.org/10.5194/nhess-22-165-2022 SN - 1561-8633 SN - 1684-9981 VL - 22 IS - 1 SP - 165 EP - 185 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Samprogna Mohor, Guilherme A1 - Thieken, Annegret A1 - Korup, Oliver T1 - Residential flood loss estimated from Bayesian multilevel models JF - Natural Hazards and Earth System Sciences N2 - Models for the predictions of monetary losses from floods mainly blend data deemed to represent a single flood type and region. Moreover, these approaches largely ignore indicators of preparedness and how predictors may vary between regions and events, challenging the transferability of flood loss models. We use a flood loss database of 1812 German flood-affected households to explore how Bayesian multilevel models can estimate normalised flood damage stratified by event, region, or flood process type. Multilevel models acknowledge natural groups in the data and allow each group to learn from others. We obtain posterior estimates that differ between flood types, with credibly varying influences of water depth, contamination, duration, implementation of property-level precautionary measures, insurance, and previous flood experience; these influences overlap across most events or regions, however. We infer that the underlying damaging processes of distinct flood types deserve further attention. Each reported flood loss and affected region involved mixed flood types, likely explaining the uncertainty in the coefficients. Our results emphasise the need to consider flood types as an important step towards applying flood loss models elsewhere. We argue that failing to do so may unduly generalise the model and systematically bias loss estimations from empirical data. KW - damage KW - insurance KW - Germany KW - transferability KW - preparedness KW - recovery Y1 - 2020 U6 - https://doi.org/10.5194/nhess-21-1599-2021 SN - 2195-9269 VL - 21 SP - 1599 EP - 1614 PB - European Geophysical Society CY - Katlenburg-Lindau ER - TY - JOUR A1 - Samprogna Mohor, Guilherme A1 - Hudson, Paul A1 - Thieken, Annegret T1 - A comparison of factors driving flood losses in households affected by different flood types JF - Water resources research N2 - 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.
Key Points
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 KW - Loss modeling KW - Riverine floods KW - Surface floods KW - Groundwater KW - Levee KW - breaches KW - Compound flood event Y1 - 2020 U6 - https://doi.org/10.1029/2019WR025943 SN - 0043-1397 SN - 1944-7973 VL - 56 IS - 4 PB - American Geophysical Union CY - Washington ER - TY - THES A1 - Samprogna Mohor, Guilherme T1 - Exploring the transferability of flood loss models across flood types N2 - The estimation of financial losses is an integral part of flood risk assessment. The application of existing flood loss models on locations or events different from the ones used to train the models has led to low performance, showing that characteristics of the flood damaging process have not been sufficiently well represented yet. To improve flood loss model transferability, I explore various model structures aiming at incorporating different (inland water) flood types and pathways. That is based on a large survey dataset of approximately 6000 flood-affected households which addresses several aspects of the flood event, not only the hazard characteristics but also information on the affected building, socioeconomic factors, the household's preparedness level, early warning, and impacts. Moreover, the dataset reports the coincidence of different flood pathways. Whilst flood types are a classification of flood events reflecting their generating process (e.g. fluvial, pluvial), flood pathways represent the route the water takes to reach the receptors (e.g. buildings). In this work, the following flood pathways are considered: levee breaches, river floods, surface water floods, and groundwater floods. The coincidence of several hazard processes at the same time and place characterises a compound event. In fact, many flood events develop through several pathways, such as the ones addressed in the survey dataset used. Earlier loss models, although developed with one or multiple predictor variables, commonly use loss data from a single flood event which is attributed to a single flood type, disregarding specific flood pathways or the coincidence of multiple pathways. This gap is addressed by this thesis through the following research questions: 1. In which aspects do flood pathways of the same (compound inland) flood event differ? 2. How much do factors which contribute to the overall flood loss in a building differ in various settings, specifically across different flood pathways? 3. How well can Bayesian loss models learn from different settings? 4. Do compound, that is, coinciding flood pathways result in higher losses than a single pathway, and what does the outcome imply for future loss modelling? Statistical analysis has found that households affected by different flood pathways also show, in general, differing characteristics of the affected building, preparedness, and early warning, besides the hazard characteristics. Forecasting and early warning capabilities and the preparedness of the population are dominated by the general flood type, but characteristics of the hazard at the object-level, the impacts, and the recovery are more related to specific flood pathways, indicating that risk communication and loss models could benefit from the inclusion of flood-pathway-specific information. For the development of the loss model, several potentially relevant predictors are analysed: water depth, duration, velocity, contamination, early warning lead time, perceived knowledge about self-protection, warning information, warning source, gap between warning and action, emergency measures, implementation of property-level precautionary measures (PLPMs), perceived efficacy of PLPMs, previous flood experience, awareness of flood risk, ownership, building type, number of flats, building quality, building value, house/flat area, building area, cellar, age, household size, number of children, number of elderly residents, income class, socioeconomic status, and insurance against floods. After a variable selection, descriptors of the hazard, building, and preparedness were deemed significant, namely: water depth, contamination, duration, velocity, building area, building quality, cellar, PLPMs, perceived efficacy of PLPMs, emergency measures, insurance, and previous flood experience. The inclusion of the indicators of preparedness is relevant, as they are rarely involved in loss datasets and in loss modelling, although previous studies have shown their potential in reducing losses. In addition, the linear model fit indicates that the explanatory factors are, in several cases, differently relevant across flood pathways. Next, Bayesian multilevel models were trained, which intrinsically incorporate uncertainties and allow for partial pooling (i.e. different groups of data, such as households affected by different flood pathways, can learn from each other), increasing the statistical power of the model. A new variable selection was performed for this new model approach, reducing the number of predictors from twelve to seven variables but keeping factors of the hazard, building, and preparedness, namely: water depth, contamination, duration, building area, PLPMs, insurance, and previous flood experience. The new model was trained not only across flood pathways but also across regions of Germany, divided according to general socioeconomic factors and insurance policies, and across flood events. The distinction across regions and flood events did not improve loss modelling and led to a large overlap of regression coefficients, with no clear trend or pattern. The distinction of flood pathways showed credibly distinct regression coefficients, leading to a better understanding of flood loss modelling and indicating one potential reason why model transferability has been challenging. Finally, new model structures were trained to include the possibility of compound inland floods (i.e. when multiple flood pathways coincide on the same affected asset). The dataset does not allow for verifying in which sequence the flood pathway waves occurred and predictor variables reflect only their mixed or combined outcome. Thus, two Bayesian models were trained: 1. a multi-membership model, a structure which learns the regression coefficients for multiple flood pathways at the same time, and 2. a multilevel model wherein the combination of coinciding flood pathways makes individual categories. The multi-membership model resulted in credibly different coefficients across flood pathways but did not improve model performance in comparison to the model assuming only a single dominant flood pathway. The model with combined categories signals an increase in impacts after compound floods, but due to the uncertainty in model coefficients and estimates, it is not possible to ascertain such an increase as credible. That is, with the current level of uncertainty in differentiating the flood pathways, the loss estimates are not credibly distinct from individual flood pathways. To overcome the challenges faced, non-linear or mixed models could be explored in the future. Interactions, moderation, and mediation effects, as well as non-linear effects, should also be further studied. Loss data collection should regularly include preparedness indicators, and either data collection or hydraulic modelling should focus on the distinction of coinciding flood pathways, which could inform loss models and further improve estimates. Flood pathways show distinct (financial) impacts, and their inclusion in loss modelling proves relevant, for it helps in clarifying the different contribution of influencing factors to the final loss, improving understanding of the damaging process, and indicating future lines of research. N2 - Die Schätzung finanzieller Schäden ist ein wesentlicher Bestandteil der Hochwasserrisikoanalyse. Die Anwendung bestehender Hochwasserschadensmodelle auf anderen Orten oder Ereignisse als jene, die zur Kalibrierung der Modelle verwendet wurden, hat zu einer geringen Modellgüte geführt. Dies zeigt, dass die Merkmale des Hochwasserschadensprozesses in den Modellen noch nicht hinreichend repräsentiert sind. Um die Übertragbarkeit von Hochwasserschadensmodellen zu verbessern, habe ich verschiedene Modellstrukturen untersucht, die darauf abzielen, unterschiedliche Hochwassertypen und wirkungspfade einzubeziehen. Dies geschieht auf der Grundlage eines großen Datensatzes von ca. 6000 Fällen überschwemmungsgeschädigter Haushalte, der mehrere Aspekte des Hochwasserereignisses berücksichtigt. Diese sind nicht nur die Gefährdungsmerkmale, sondern auch Informationen über das betroffene Gebäude, sozioökonomische Faktoren, die Vorsorge des Haushalts, die Frühwarnung und die Auswirkungen. Darüber hinaus enthält der Datensatz Informationen über das Vorkommen verschiedener Hochwasserwirkungspfade. Im Gegensatz zu den Hochwassertypen, die eine Klassifizierung von Hochwasserereignissen darstellen und deren Entstehungsprozess widerspiegeln (z. B. Fluss- oder Regenhochwasser), repräsentieren die Hochwasserwirkungspfade den Weg, den das Wasser nimmt, um die Rezeptoren (z. B. die Gebäude) zu erreichen. In dieser Arbeit werden folgende Hochwasserwirkungspfade betrachtet: Deichbrüche, Flusshochwasser, Überflutung durch oberflächlich abfließendes Wasser und Grundwasserhochwasser. Das Zusammentreffen mehrerer Gefahrenprozesse zur selben Zeit und am selben Ort kennzeichnet ein Verbundereignis (compound event). Tatsächlich entwickeln sich viele Hochwasserereignisse über mehrere Wirkungspfade, z. B. die vorher erwähnten. Frühere Schadensmodelle, die zwar mit einer oder mehreren Prädiktorvariablen entwickelt wurden, verwenden in der Regel Schadensdaten eines einzelnen Hochwasserereignisses, das einem bestimmten Hochwassertyp zugeordnet wird. Spezifische Hochwasserwirkungspfade oder das Zusammentreffen mehrerer Wirkungspfade werden dabei vernachlässigt. An dieser Forschungslücke setzt die vorliegende Arbeit mit folgenden Forschungsfragen an: 1) Inwiefern unterscheiden sich die Hochwasserwirkungspfade desselben (zusammengesetzten) Hochwasserereignisses? 2) Inwieweit unterscheiden sich die Faktoren, die zum gesamten Hochwasserschaden an einem Gebäude beitragen, in verschiedenen Situationen, insbesondere bei verschiedenen Hochwasserwirkungspfaden? 3) Wie gut können Bayes'sche Schadensmodelle aus verschiedenen Situationen lernen? 4) Führen gemischte, d. h. mehrere zusammentreffende Hochwasserwirkungspfade, zu höheren Schäden als ein einzelner Pfad und was bedeuten die Ergebnisse für die künftige Schadensmodellierung? Die statistische Analyse zeigt, dass Haushalte, die von verschiedenen Hochwasserwirkungspfaden betroffen sind, im Allgemeinen neben den Gefahrenmerkmalen auch unterschiedliche Eigenschaften des betroffenen Gebäudes sowie der Vorsorge und der Frühwarnung aufweisen. Die Variablen des Frühwarnsystems und die Vorsorge der Bevölkerung werden von dem allgemeinen Hochwassertyp dominiert, wohingegen die Merkmale der Gefahr auf Objektebene, die Auswirkungen und die Wiederherstellung von den spezifischeren Hochwasserwirkungspfaden dominiert. Dies deutet darauf hin, dass Risikokommunikation und Schadensmodelle von der Einbeziehung hochwasserwirkungspfad-spezifischer Informationen profitieren könnten. Für die Entwicklung des Schadensmodells wurden mehrere potenziell relevante Prädiktoren analysiert: Wassertiefe, Dauer, Geschwindigkeit, Verschmutzung, Vorwarnzeit, wahrgenommenes Wissen über Selbstschutz, Warninformation, Warnquelle, Zeitspanne zwischen Warnung und Handlung, Notfallmaßnahmen, Umsetzung von Vorsorgemaßnahmen auf Grundstücksebene (PLPMs), wahrgenommene Wirksamkeit von PLPMs, frühere Hochwassererfahrungen, Bewusstsein für das Hochwasserrisiko, Eigentumsverhältnisse, Gebäudetyp, Anzahl der Wohnungen, Gebäudequalität, Gebäudewert, Haus-/Wohnungsfläche, Gebäudefläche, Keller, Alter der befragten Person, Haushaltsgröße, Anzahl der Kinder, Anzahl der älteren Menschen, monatliches Einkommen sowie sozioökonomischer Status und Versicherung gegen Hochwasser. Nach einer Variablenauswahl wurden folgende Deskriptoren der Gefahr, des Gebäudes und der Vorbereitung als signifikant eingestuft: Wassertiefe, Verschmutzung, Überflutungsdauer, Geschwindigkeit, Gebäudefläche, Gebäudequalität, Keller, PLPMs, wahrgenommene Wirksamkeit von PLPMs, Notfallmaßnahmen, Versicherung und frühere Hochwassererfahrung. Die Einbeziehung der letztgenannten Gruppe von Faktoren ist von Bedeutung, da Indikatoren für die Vorsorge nur selten in Schadensdatensätze und Schadensmodellierung integriert werden, obwohl frühere Studien gezeigt haben, dass sie zur Verringerung von Schäden beitragen können. Die lineare Modellanpassung zeigte, dass die erklärenden Faktoren in mehreren Fällen je nach Hochwasserpfad unterschiedlich relevant sind. Als Nächstes wurden Bayes'sche Mehrebenenmodelle trainiert, die Unsicherheiten immanent einbeziehen und ein partielles Pooling ermöglichen. Das heißt, verschiedene Datengruppen (Haushalte, die von verschiedenen Hochwasserwirkungspfaden betroffen sind) können voneinander lernen, was die statistische Aussagekraft des Modells erhöht. Für diesen neuen Modellansatz wurde eine aktualisierte Variablenauswahl getroffen, bei der die Anzahl der Prädiktoren von zwölf auf sieben reduziert wurde, aber Faktoren der Gefahr, des Gebäudes und der Vorbereitung beibehalten wurden. Diese sind Wassertiefe, Verschmutzung, Dauer, Gebäudefläche, PLPMs, Versicherung und frühere Hochwassererfahrung. Das neue Modell wurde nicht nur über Hochwasserwirkungspfade, sondern auch über Regionen in Deutschland – unterteilt nach allgemeinen sozioökonomischen Faktoren und Versicherungspolicen – sowie über Hochwasserereignisse trainiert. Die Unterscheidung nach Regionen und Hochwasserereignissen verbesserte die Schadensmodellierung nicht und führte zu einer großen Überlappung der Regressionskoeffizienten ohne klaren Trend oder eindeutiges Muster. Die Unterscheidung nach Hochwasserwirkungspfaden ergab glaubhaft unterschiedliche Regressionskoeffizienten, was zu einem besseren Verständnis der Modellierung von Hochwasserschäden führte und einen möglichen Grund für die schwierige Übertragbarkeit der Modelle auf andere Situationen darstellt. Schließlich wurden neue Modellstrukturen trainiert, um die Möglichkeit gemischter (Binnen)überschwemmungen, d. h. das Zusammentreffen mehrerer Hochwasserwirkungspfade auf demselben Objekt, zu berücksichtigen. Anhand des Datensatzes lässt sich nicht überprüfen, in welcher Reihenfolge die Hochwasserpfadwellen auftraten, und die Prädiktorvariablen zeigen nur deren gemischtes oder kombiniertes Ergebnis. Daher wurden zwei Bayes'sche Modelle trainiert: 1) ein Multi-Membership-Modell als Struktur, die die Regressionskoeffizienten für mehrere Hochwasserwirkungspfade gleichzeitig lernt, und 2) ein Mehrebenenmodell, bei dem die Kombination zusammentreffender Hochwasserwirkungspfade einzelne Kategorien bildet. Ersteres führte zu glaubhaft unterschiedlichen Koeffizienten für die verschiedenen Hochwasserwirkungspfade, verbesserte aber nicht die Modellleistung im Vergleich zu dem Modell, das nur einen einzigen, dominanten Hochwasserpfad annimmt. Das Modell mit kombinierten Wirkungspfadkategorien deutet auf eine Zunahme der Auswirkungen nach gemischten Überschwemmungen hin. Aufgrund der Unsicherheit der Modellkoeffizienten und -schätzungen ist es jedoch nicht möglich, eine solche Zunahme als glaubwürdig plausibel zu bewerten. Das heißt, bei dem derzeitigen Grad an Unsicherheit hinsichtlich der Differenzierung der Hochwasserwirkungspfade sind die Schadensschätzungen nicht glaubwürdig von den einzelnen Hochwasserwirkungspfaden zu unterscheiden. Zur Überwindung der bestehenden Probleme könnten nichtlineare oder gemischte Modelle untersucht werden. Zudem sollten Interaktionseffekte, Moderations- und Mediationseffekte sowie nichtlineare Effekte weiter erforscht werden. Bei der Schadensdaten\-erhebung sollten außerdem regelmäßig Indikatoren für die Vorsorge einbezogen werden, und entweder bei der Datenerhebung oder bei der hydraulischen Modellierung sollte der Schwerpunkt auf der Unterscheidung kombinierter Hochwasserwirkungspfade liegen, was die Schadensmodelle bereichern und die Schätzungen weiter verbessern könnte. Hochwasserwirkungspfade zeigen differente (finanzielle) Auswirkungen und ihre Einbeziehung in die Schadensmodellierung hat sich als relevant erwiesen, da sie dazu beitragen, den unterschiedlichen Beitrag der Einflussfaktoren zum endgültigen Schaden zu klären, das Verständnis des Schadensprozesses zu verbessern und künftige Forschungslinien aufzuzeigen. T2 - Untersuchung der Übertragbarkeit von Hochwasserschadensmodellen über Hochwassertypen KW - flood KW - financial loss KW - flood loss modelling KW - Bayesian model KW - multilevel modelling KW - flood pathway KW - Hochwasser KW - finanzielle Schäden KW - Schätzung finanzieller Schäden KW - Bayes'sche Modelle KW - Mehrebenenmodelle KW - Hochwasserwirkungspfad Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-557141 ER - TY - GEN A1 - Samprogna Mohor, Guilherme A1 - Thieken, Annegret A1 - Korup, Oliver T1 - Residential flood loss estimated from Bayesian multilevel models T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Models for the predictions of monetary losses from floods mainly blend data deemed to represent a single flood type and region. Moreover, these approaches largely ignore indicators of preparedness and how predictors may vary between regions and events, challenging the transferability of flood loss models. We use a flood loss database of 1812 German flood-affected households to explore how Bayesian multilevel models can estimate normalised flood damage stratified by event, region, or flood process type. Multilevel models acknowledge natural groups in the data and allow each group to learn from others. We obtain posterior estimates that differ between flood types, with credibly varying influences of water depth, contamination, duration, implementation of property-level precautionary measures, insurance, and previous flood experience; these influences overlap across most events or regions, however. We infer that the underlying damaging processes of distinct flood types deserve further attention. Each reported flood loss and affected region involved mixed flood types, likely explaining the uncertainty in the coefficients. Our results emphasise the need to consider flood types as an important step towards applying flood loss models elsewhere. We argue that failing to do so may unduly generalise the model and systematically bias loss estimations from empirical data. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1148 KW - damage KW - insurance KW - Germany KW - transferability KW - preparedness KW - recovery Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-517743 SN - 1866-8372 SP - 1599 EP - 1614 ER - TY - RPRT A1 - Berghäuser, Lisa A1 - Schoppa, Lukas A1 - Ulrich, Jana A1 - Dillenardt, Lisa A1 - Jurado, Oscar E. A1 - Passow, Christian A1 - Samprogna Mohor, Guilherme A1 - Seleem, Omar A1 - Petrow, Theresia A1 - Thieken, Annegret T1 - Starkregen in Berlin BT - Meteorologische Ereignisrekonstruktion und Betroffenenbefragung N2 - In den Sommern der Jahre 2017 und 2019 kam es in Berlin an mehreren Orten zu Überschwemmungen in Folge von Starkregenereignissen. In beiden Jahren führte dies zu erheblichen Beeinträchtigungen im Alltag der Berliner:innen sowie zu hohen Sachschäden. Eine interdisziplinäre Taskforce des DFG-Graduiertenkollegs NatRiskChange untersuchte (1) die meteorologischen Eigenschaften zweier besonders eindrücklicher Unwetter, sowie (2) die Vulnerabilität der Berliner Bevölkerung gegenüber Starkregen. Eine vergleichende meteorologische Rekonstruktion der Starkregenereignisse von 2017 und 2019 ergab deutliche Unterschiede in der Entstehung und den Überschreitungswahrscheinlichkeiten der beiden Unwetter. So war das Ereignis von 2017 mit einer relativ großen räumlichen Ausdehnung und langer Dauer ein untypisches Starkregenereignis, während es sich bei dem Unwetter von 2019 um ein typisches, kurzzeitiges Starkregenereignis mit ausgeprägter räumlicher Heterogenität handelte. Eine anschließende statistische Analyse zeigte, dass das Ereignis von 2017 für längere Niederschlagsdauern (>=24 h) als großflächiges Extremereignis mit Überschreitungswahrscheinlichkeiten von unter 1 % einzuordnen ist (d.h. Wiederkehrperioden >=100 Jahre). Im Jahr 2019 wurden dagegen ähnliche Überschreitungswahrscheinlichkeiten nur lokal und für kürzere Zeiträume (1-2 h) berechnet. Die Vulnerabilitätsanalyse basiert auf einer von April bis Juni 2020 in Berlin durchgeführten Onlinebefragung. Diese richtete sich an Personen, die bereits von vergangenen Starkregenereignissen betroffen waren und thematisierte das Schadensereignis selbst, daraus entstandene Beeinträchtigungen und Schäden, Risikowahrnehmung sowie Notfall- und Vorsorgemaßnahmen. Die erhobenen Umfragedaten (n=102) beziehen sich vornehmlich auf die Ereignisse von 2017 und 2019 und zeigen, dass die Berliner Bevölkerung sowohl im Alltag (z.B. bei der Beschaffung von Lebensmitteln) als auch im eigenen Haushalt (z.B. durch Überschwemmungsschäden) von den Unwettern beeinträchtigt war. Zudem deuteten die Antworten der Betroffenen auf Möglichkeiten hin, die Vulnerabilität der Gesellschaft gegenüber Starkregen weiter zu reduzieren - etwa durch die Unterstützung besonders betroffener Gruppen (z.B. Pflegende), durch gezielte Informationskampagnen zum Schutz vor Starkregen oder durch die Erhöhung der Reichweite von Unwetterwarnungen. Eine statistische Analyse zur Effektivität privater Notfall- und Vorsorgemaßnahmen auf Grundlage der Umfragedaten bestätigte vorherige Studienergebnisse. So gab es Anhaltspunkte dafür, dass durch das Umsetzen von Vorsorgemaßnahmen wie beispielsweise das Installieren von Rückstauklappen, Barriere-Systemen oder Pumpen Starkregenschäden reduziert werden können. Die Ergebnisse dieses Berichts unterstreichen die Notwendigkeit für ein integriertes Starkregenrisikomanagment, das die Risikokomponenten Gefährdung, Vulnerabilität und Exposition ganzheitlich und auf mehreren Ebenen (z.B. staatlich, kommunal, privat) betrachtet. N2 - In the summers of 2017 and 2019, the city of Berlin was hit by heavy rainfall leading to urban flooding in several locations. In both years, this led to considerable disruptions of the daily life and high property damage. With focus on two particularly impressive events a taskforce of the DFG Research Training Group NatRiskChange investigated (1) the meteorological characteristics of both events as well as (2) the vulnerability of the Berlin population to heavy rainfall. A comparative meteorological reconstruction of the 2017 and 2019 heavy rainfall events revealed fundamental differences between the two storms. The 2017 event was an atypical heavy rain event, as it was characterized by a relatively large spatial extent and long duration of rainfall, whereas the 2019 storm was a typical short duration heavy rain event with a distinct spatial heterogeneity. Subsequent statistical analysis indicated that the 2017 event should be classified as a large-scale extreme event with exceedance probabilities below 1 % for longer precipitation durations (i.e., return periods of over 100 years). In contrast, in 2019 similar exceedance probabilities were estimated only locally and for shorter durations (1-2 h). The vulnerability analysis of this taskforce was based on an online survey conducted in Berlin between April and June 2020. The survey was aimed at people who had experienced past heavy rainfall events in Berlin, and addressed the resulting impairments and damages, risk perceptions as well as emergency and preparedness measures. The survey data (n=102) primarily referred to the events of 2017 and 2019 and showed that the respondents were affected by the storms both in their daily lives (e.g., when purchasing food) and in their own households (e.g., due to flood damage). In addition, the analysis of the responses pointed to ways to further reduce society's vulnerability to heavy rain. That was, for example, by providing support to particularly affected groups (e.g., caregivers), through targeted information campaigns to protect against heavy rainfall or by improving the range of early warning systems. A statistical analysis of the efficacy of property-level emergency and preparedness measures based on the survey data confirmed previous study findings and provided evidence of reducing heavy rain damage through preparedness. The findings of the taskforce highlight the need for integrated heavy rainfall risk management that considers the risk components of hazard, vulnerability, and exposure holistically and at multiple levels (e.g., state, local and private households). KW - Starkregen KW - Risikomanagement KW - Meteorologische Ereignisanalyse KW - Betroffenenbefragung KW - Berlin KW - Urban Flooding KW - Risk reduction KW - Meteorological Event Analysis KW - Survey of affected residents KW - Berlin Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-500560 ER - TY - JOUR A1 - Guzman Arias, Diego Alejandro A1 - Samprogna Mohor, Guilherme A1 - Mendiondo, Eduardo Mario T1 - Multi-driver ensemble to evaluate the water utility business interruption cost induced by hydrological drought risk scenarios in Brazil JF - Urban water journal N2 - Climate change and increasing water demand in urban environments necessitate planning water utility companies' finances. Traditionally, methods to estimate the direct water utility business interruption costs (WUBIC) caused by droughts have not been clearly established. We propose a multi-driver assessment method. We project the water yield using a hydrological model driven by regional climate models under radiative forcing scenarios. We project water demand under stationary and non-stationary conditions to estimate drought severity and duration, which are linked with pricing policies recently adopted by the Sao Paulo Water Utility Company. The results showed water insecurity. The non-stationary trend imposed larger differences in the drought resilience financial gap, suggesting that the uncertainties of WUBIC derived from demand and climate models are greater than those associated with radiative forcing scenarios. As populations increase, proactively controlling demand is recommended to avoid or minimize reactive policy changes during future drought events, repeating recent financial impacts. KW - Business interruption cost KW - water utility company KW - hydrological KW - droughts KW - water security KW - urban water KW - climate change Y1 - 2022 U6 - https://doi.org/10.1080/1573062X.2022.2058564 SN - 1573-062X SN - 1744-9006 PB - Routledge, Taylor & Francis Group CY - Abingdon ER - TY - JOUR A1 - Arguello de Souza, Felipe Augusto A1 - Samprogna Mohor, Guilherme A1 - Guzman Arias, Diego Alejandro A1 - Sarmento Buarque, Ana Carolina A1 - Taffarello, Denise A1 - Mendiondo, Eduardo Mario T1 - Droughts in São Paulo BT - challenges and lessons for a water-adaptive society JF - Urban water journal N2 - Literature has suggested that droughts and societies are mutually shaped and, therefore, both require a better understanding of their coevolution on risk reduction and water adaptation. Although the Sao Paulo Metropolitan Region drew attention because of the 2013-2015 drought, this was not the first event. This paper revisits this event and the 1985-1986 drought to compare the evolution of drought risk management aspects. Documents and hydrological records are analyzed to evaluate the hazard intensity, preparedness, exposure, vulnerability, responses, and mitigation aspects of both events. Although the hazard intensity and exposure of the latter event were larger than the former one, the policy implementation delay and the dependency of service areas in a single reservoir exposed the region to higher vulnerability. In addition to the structural and non-structural tools implemented just after the events, this work raises the possibility of rainwater reuse for reducing the stress in reservoirs. KW - droughts KW - urban water supply KW - water crisis KW - drought risk KW - paired event KW - analysis KW - vulnerability Y1 - 2022 U6 - https://doi.org/10.1080/1573062X.2022.2047735 SN - 1573-062X SN - 1744-9006 VL - 20 IS - 10 SP - 1682 EP - 1694 PB - Taylor & Francis CY - London [u.a.] ER -