In this study, Hazus-MH (v 2.1 SP 2) flood-loss estimation tools were assessed for their sensitivity to an array of different building and model parameters. The purpose of this study is to help guide users of the Hazus-MH flood-loss modeling tool in the selection of most appropriate model parameters. Six model parameters (square footage of the building, building age, construction types, foundation types, first floor heights, and the number of stories in the building) were assessed for their impacts on flood losses using the Hazus-MH user defined and aggregate flood-loss models. Building stock databases for these analyses were developed using county assessor records from two Illinois counties. A validation assessment was also performed using observed flood-damage survey data collected after the 2011 Mississippi River Flood which inundated the Olive Branch Area in Alexander County, Illinois. This analysis was performed to assess the accuracy of the detailed Hazus-MH User Defined Facility (UDF) flood-loss modeling tool. The foundation types and its associated first floor heights and number of stories in the building were found to substantially impact flood-loss estimates using the Hazus-MH flood-loss modeling tool. The model building parameters square footage, building age and construction type had little or no effect on the flood-loss estimates. The validation assessment reveled Hazus-MH UDF flood-loss modeling tool is capable of providing a reasonable estimate of actual flood losses. The validation assessment showed the modeled results to be within 23% of actual losses. The validation study results attained in this study using the detailed UDF flood-loss modeling tool where more realistic (within 23% of actual losses versus > 50% of actual losses) than previous Hazus-MH flood-loss validation assessments. The flood-loss estimates could be further improved by modifying or choosing a more region specific depth-damage curve, using higher resolution DEM and improving the flood-depth grid by incorporating more detailed flood elevation data or estimates using detailed hydraulic models that better reflects the local inundation conditions.

This study aims at developing a model to estimate flood damage cost caused in Gonaives, Haiti by Hurricane Jeanne in 2004. In order to reach this goal, the influence of income, inundation duration and inundation depth, slope, population density and distance to major roads on the loss costs was investigated. Surveyed data were analyzed using Excel and ArcGIS 10 software. The ordinary least square and the geographically weighted regression analyses were used to predict flood damage costs. Then, the estimates were delineated using voronoi geostatistical map tool. As a result, the factors account for the costs as high as 83%. The flood damage cost in a household varies between 24,315 through 37,693 Haitian Gourdes (approximately 607.875 through 942.325 U.S. Dollars). Severe damages were spotted in the urban area and in the rural section of Bassin whereas very low and low losses are essentially found in Labranle. The urban area was more severely affected by comparison with the rural area. Damages in the urban area are estimated at 41,206,869.57USD against 698,222,174.10 17,455,554.35USD in the rural area. In the urban part, damages were more severe in Raboteau-Jubilée and in Downtown but Bigot-Parc Vincent had the highest overall damage cost estimated at 9,729,368.95 USD. The lowest cost 7,602,040.42USD was recorded in Raboteau. Approximately, 39.38% of the rural area underwent very low to moderate damages. Bassin was the most severely struck by the 2004 floods, but Bayonnais turned out to have the highest loss cost: 4,988,487.66 USD. Bassin along with Labranle had the least damage cost, 2,956,131.11 and 2,268,321.41 USD respectively. Based on the findings, we recommended the implementation and diversification of income-generating activities, the maintenance and improvement of drains, sewers and gullies cleaning and the establishment of conservation practices upstream of the watersheds. In addition, the model should be applied and validated using actual official records as reference data. Finally, the use of a calculation-based approach is suggested to determine flood damage costs in order to reduce subjectivity during surveys.

I Sverige inträffar många skyfall och intensiva regn under sommarmånaderna. Det finns inga uppenbara geografiska mönster, vilket är en skillnad gentemot älv- eller sjööversvämningar där det vanligtvis är känt vilka områden som kan komma att översvämmas vid en viss vattennivå eller ett visst vattenflöde. För individer och samhällsaktörer innebär en skyfallshändelse i många fall en stor överraskning då skyfall utvecklas snabbt och dagens meteorologiska prognossystem i stort inte lyckas att prognosticera extrema regn korrekt med avseende på mängd, tid och plats. Vädervarningar kommer med kort varsel eller uteblir helt. Konsekvenserna av intensiv nederbörd och skyfall är främst översvämningar och erosionsskador på byggnader och infrastruktur, men även störningar och avbrott i olika samhällsfunktioner som kan påverka samhället och individer utanför det drabbade området. I denna avhandling har 15 år av försäkringsskadedata använts för att undersöka samband mellan nederbördsintensitet och skyfallsskador. Även påverkan av andra faktorer som topografi, bebyggelse och socioekonomiska aspekter har undersökts. Resultaten visar att regnintensitet under ett 60 minuters intervall i kombination med korta perioder av extrem intensitet, tillsammans med topografiska faktorer spelar en betydande roll vid uppkomsten av skador.

Hochwasserschadensprozesse werden von den drei Komponenten des Hochwasserrisikos bestimmt – der Gefahr, der Exposition und der Vulnerabilität. Dabei bleiben wichtige Einflussgrößen auf die Vulnerabilität, wie die private Hochwasservorsorge aufgrund fehlender quantitativer Informationen unberücksichtigt. Diese Arbeit entwickelt daher eine robuste statistische Methode zur Quantifizierung des Einflusses von privater Hochwasservorsorge auf die Reduzierung der Vulnerabilität von Haushalten bei Hochwasser. Es konnte gezeigt werden, dass in Deutschland private Hochwasservorsorgemaßnahmen den durchschnittlichen Hochwasserschaden pro Wohngebäude um 11.000 bis 15.000 Euro reduzieren. Hochwasserschadensmodelle mit Expertenwissen und datengestützten Methoden sind dabei am besten in der Lage Unterschiede in der Vulnerabilität durch private Hochwasservorsorge zu erkennen. Die über Hochwasserschadenprozesse erhobenen Daten und Modellannahmen sind von Unsicherheit geprägt und so sind auch Schätzungen mit. Die Bayesschen Modelle, die in dieser Arbeit entwickelt und angewandt werden, nutzen Annahmen über Schadensprozesse als Prior und empirische Daten zur Aktualisierung der Wahrscheinlischkeitsverteilungen. Die Modelle bieten Hochwasserschadensschätzungen als Verteilung, welche die Bandbreite der Variabilität der Schadensprozesse und die Unsicherheit der Modellannahmen abbilden. Hochwasserschadensmodelle, hinsichtlich der Prognoseerstellung und Anwendbarkeit. Ins Besondere verbessert die Verwendung einer Beta–Verteilung die Zuverlässigkeit der Modellergebnisse im Vergleich zu den häufig genutzten Gaußschen oder nicht parametrischen Verteilungen. Der hierarchische Bayessche Ansatz schafft eine verbesserte Parametrisierung von Wasserstand-Schadens-Funktionen und ersetzt so die Notwendigkeit empirischer Daten durch regional- und Ereignis-spezifisches Expertenwissen. Auf diese Weise kann die Vorhersage bei einer zeitlich und räumlichen Übertragung des Models verbessert werden. / Flood damage processes are influenced by the three components of flood risk - hazard, exposure and vulnerability. In comparison to hazard and exposure, the vulnerability component, though equally important is often generalized in many flood risk assessments by a simple depth-damage curve. Hence, this thesis developed a robust statistical method to quantify the role of private precaution in reducing flood vulnerability of households. In Germany, the role of private precaution was found to be very significant in reducing flood damage (11 - 15 thousand euros, per household). Also, flood loss models with structure, parameterization and choice of explanatory variables based on expert knowledge and data-driven methods were successful in capturing changes in vulnerability, which makes them suitable for future risk assessments. Due to significant uncertainty in the underlying data and model assumptions, flood loss models always carry uncertainty around their predictions. This thesis develops Bayesian approaches for flood loss modelling using assumptions regarding damage processes as priors and available empirical data as evidence for updating. Thus, these models provide flood loss predictions as a distribution, that potentially accounts for variability in damage processes and uncertainty in model assumptions. The models presented in this thesis are an improvement over the state-of-the-art flood loss models in terms of prediction capability and model applicability. In particular, the choice of the response (Beta) distribution improved the reliability of loss predictions compared to the popular Gaussian or non-parametric distributions; the Hierarchical Bayesian approach resulted in an improved parameterization of the common stage damage functions that replaces empirical data requirements with region and event-specific expert knowledge, thereby, enhancing its predictive capabilities during spatiotemporal transfer.