Floods are the most common global natural disaster, and 1 billion people live in floodplains worldwide adding to the impactful damage that inundation causes. Disaster managers strive to mitigate damages to their communities but need to know what the impact of a potential flood may be. GEOGloWS ECMWF Streamflow Services estimates forecasted streamflow around the world. These forecasted streamflow's can be used to create predicted flood extent maps using Height Above Nearest Drainage (HAND) or Sedimentation and River Hydraulics - Two Dimension (SRH-2D). Another method to obtain a flood map is using Setinel-1 satellite Synthetic Aperture Radar (SAR) imagery. Flood maps alone will not demonstrate the impact of the flood, but some exposure data will provide needed impact metrics. In this research, I wanted to produce a general geoprocessing method for stakeholders to compute flood impact metrics over any flood extent map using any exposure dataset. Additionally, I sought to create similar geoprocessing workflows in ArcGIS Pro, QGIS, and stand-alone Python script so that the stakeholders can choose the best suited method that correlates with their access and familiarity. The general geoprocessing workflow was tested using three different global exposure datasets (Agriculture, Infrastructure, and Population). The three different geoprocessing implementations were tested in three areas that are of concern in the greater NASA SERVIR organization using the same flood map and exposure datasets for each area. This research produced a feasible, sustainable, successful, generalized geoprocessing workflow that computes flood impact metrics from a flood map and global exposure datasets. The global datasets can be interchanged with higher resolution exposure datasets specific to an area of interest generating more accurate results. The three geoprocessing methods performed similarly. The results were slightly different when the exposure dataset was a raster file as the conversion from raster to vector format produced differences in rounding values and programming implementation. However, this research's findings are such that the three geoprocessing methods are comparable and that any of the three geoprocessing implementations will produce reasonably similar flood impact results. Ongoing work by the Brigham Young University (BYU) Hydroinformatics lab is to create a Tethys web application that will allow stakeholders to view the flood map and flood impact of areas of interest. Future work may include investigating the workflow workability on a global scale, discovering and implementing global exposure data sources of better resolution, researching more data metrics that can contribute to a more robust flood impact results, and increasing the accuracy of flood impact results when compared among ArcGIS Pro, QGIS, and Python.

<p>  </p> <p>Floods all over the world are one of the most common and devastating natural disasters for human society, and the flood risk is increasing recently due to more and more extreme climatic events. In the United States, one of the key resources that provide the flood risk information to the public is the Flood Insurance Rate Map (FIRM) administrated by the Federal Emergency Management Agency (FEMA) and the digitalized FIRMs have covered over 90% of the United States population so far. However, the uncertainty in the modeling process of FIRMs is rarely investigated. In this study, we use two of the widely used multi-model methods, the Bayesian Model Averaging (BMA) and the generalized likelihood uncertainty estimation (GLUE), to evaluate and reduce the impacts of various uncertainties with respect to modeling settings, evaluation metrics, and algorithm parameters on the flood modeling of FIRMs. Accordingly, three objectives of this study are to: (1) quantify the uncertainty in FEMA FIRMs by using BMA and Hierarchical BMA approaches; (2) investigate the inherent limitations and uncertainty in existing evaluation metrics of flood models; and (3) estimate the BMA parameters (weights and variances) using the Metropolis-Hastings (M-H) algorithm with multiple Markov Chains Monte Carlo (MCMC).</p> <p><br></p> <p>In the first objective, both the BMA and hierarchical BMA (HBMA) approaches are employed to quantify the uncertainty within the detailed FEMA models of the Deep River and the Saint Marys River in the State of Indiana based on water stage predictions from 150 HEC-RAS 1D unsteady flow model configurations that incorporate four uncertainty sources including bridges, channel roughness, floodplain roughness, and upstream flow input. Given the ensemble predictions and the observed water stage data in the training period, the BMA weight and the variance for each model member are obtained, and then the BMA prediction ability is validated for the observed data from the later period. The results indicate that the BMA prediction is more robust than both the original FEMA model and the ensemble mean. Furthermore, the HBMA framework explicitly shows the propagation of various uncertainty sources, and both the channel roughness and the upstream flow input have a larger impact on prediction variance than bridges. Hence, it provides insights for modelers into the relative impact of individual uncertainty sources in the flood modeling process. The results show that the probabilistic flood maps developed based on the BMA analysis could provide more reliable predictions than the deterministic FIRMs.</p> <p><br></p> <p>In the second objective, the inherent limitations and sampling uncertainty in several commonly used model evaluation metrics, namely, the Nash Sutcliffe efficiency (<em>NSE</em>), the Kling Gupta efficiency (<em>KGE</em>), and the coefficient of determination (<em>R</em>2), are investigated systematically, and hence the overall performance of flood models can be evaluated in a comprehensive way. These evaluation metrics are then applied to the 1D HEC-RAS models of six reaches located in the states of Indiana and Texas of the United States to quantify the uncertainty associated with the channel roughness and upstream flow input. The results show that the model performances based on the uniform and normal priors are comparable. The distributions of these evaluation metrics are significantly different for the flood model under different high-flow scenarios, and it further indicates that the metrics should be treated as random statistical variables given both aleatory and epistemic uncertainties in the modeling process. Additionally, the white-noise error in observations has the least impact on the evaluation metrics.</p> <p><br></p> <p>In the third objective, the Metropolis-Hastings (M-H) algorithm, which is one of the most widely used algorithms in the MCMC method, is proposed to estimate the BMA parameters (weights and variances), since the reliability of BMA parameters determines the accuracy of BMA predictions. However, the uncertainty in the BMA parameters with fixed values, which are usually obtained from the Expectation-Maximization (EM) algorithm, has not been adequately investigated in BMA-related applications over the past few decades. Both numerical experiments and two practical 1D HEC-RAS models in the states of Indiana and Texas of the United States are employed to examine the applicability of the M-H algorithm with multiple independent Markov chains. The results show that the BMA weights estimated from both algorithms are comparable, while the BMA variances obtained from the M-H MCMC algorithm are closer to the given variances in the numerical experiment. Overall, the MCMC approach with multiple chains can provide more information associated with the uncertainty of BMA parameters and its performance of water stage predictions is better than the default EM algorithm in terms of multiple evaluation metrics as well as algorithm flexibility.</p>

The diploma thesis is focused on Location Based Services technology and its use in the field of citizen activism. The aim of thesis is to define field of citizen activism and its old and new form, moreover to describe how LBS work. In the practical part is presented multi-case study which analyses 5 examples of use LBS as a medium for new forms of civic activism (project Let's Do It 2008, project Uchaguzi, project ESRI Australian Flood Map, application Appapa and Occupy Wall Street movement). Thesis also contained conceptual draft of application, which uses LBS for fulfilment of activist goals. The possible ways of progress of LBS are described in the last part.

Begreppet översvämningar har länge varit ett väl diskuterat ämne inom den akademiska världen och har även nu på senare år uppmärksammats alltmer i nyheter samt av allmänheten i sin helhet. Översvämningar är inte längre ett naturfenomen som kan anses inträffa vid sällsynta tillfällen. Den ökade globala uppvärmningen och det förändrade klimatet spås ge en ökning av extrema nederbördstillfällen. Så sent som 17–18 augusti 2021 drabbades stora delar av Gävle av översvämningar till följd av ett extremt skyfall. Kunskap om och förmågan att kunna hantera dessa extremfenomen är väsentligt för framtidens samhälle. Med detta i åtanke har denna studie undersökt hur översvämningsutbredningen och utbredningen på tillhörande osäkerhetszoner skiljer sig beroende på framställningssätt. Med hjälp av HEC-RAS har både endimensionell (1D) och tvådimensionell (2D) hydrauliska modeller använts för att simulera den översvämning som drabbade Gävle. Studien har utförts som en fallstudie över Västra Kungsbäckens vattendrag. Global Navigation Satellite Systems (GNSS) har använts för att skaffa noggrannare batymetrisk data. Genom en korrigerad höjdmodell och fotografier från översvämningen från den 18 augusti 2021 kunde modellerna kalibreras. Därefter utfördes flödesmodellering och vidare framställdes översvämningskartor och översvämningskartor med osäkerhetszoner. Resultatet visar att den utbredning översvämningen fick beroende på modell var mycket lika. Viss skillnad föreligger i geometrin men den procentuella utbredningen (2,3 %, 5 837 m2) är minimal. När det kommer till osäkerhetszonsutbredningen framgår den totala ytan väldigt lika mellan modellerna men däremot förekommer en större skillnad i geometrin för de två områdena; säkert att översvämmas och osäkert att översvämmas mellan modellerna. Utifrån studiens förutsättningar har ett tillförlitligt resultat tagits fram där fältstudien med mätningen har förbättrat tillförlitligheten på höjddata och kalibreringen av modellen har gjorts utifrån väl beprövade metoder i litteraturen. Ytterligare validering har gjorts mot en tidigare studies resultat och slutsatsen som kan dras är att modellerna är tillförlitliga. Likt all framställning av kartor förekommer det även osäkerheter i denna studie och fler studier över andra områden krävs för att fastställa hur en endimensionell modell skiljer sig mot en tvådimensionell ur ett utbredningsperspektiv. För osäkerhetsutbredningen krävs det fler studier med 2D-data för att kunna bekräfta de antaganden som gjorts. / The concept of floods has long been a well discussed topic in the academic world and has even now in recent years received increasing attention in the news and also by the general public. Floods are no longer a natural phenomenon that can be considered to occur on rare occasions. The increased global warming and the changing climate are predicted to result in an increase in extreme cloudbursts. As recently as 17–18 August 2021, large parts of Gävle were affected by floods as a result of an extreme downpour. Knowledge of and the ability to deal with these extreme phenomena is essential for cities of the future. With this in mind, this study has examined how the prevalence of flooding and the prevalence of associated zones of uncertainty differ depending on the method of production for flood maps. With the help of HEC-RAS, both one-dimensional (1D) and two-dimensional (2D) hydraulic models were used to simulate the flood that affected Gävle. The study has been carried out as a case study of the western part of the stream Kungsbäcken. Global Navigation Satellite Systems (GNSS) has been used to get more accurate bathymetric data. Through a corrected elevation model and photographs from the flood of 18 August 2021, the models could be calibrated. Subsequently, flow modeling was performed and further flood maps and flood maps with uncertainty zones were produced. The results show that the extent of the flood depending on the model was very similar. There are some differences in the geometry, but percentage wise, the extent difference (2,3 %, 5 837 m2) is minimal. When it comes to the uncertainty zones produced, the total areal extent is very similar between the models, but there is a greater difference in the geometry for the two areas; certain to be flooded areas and uncertain to be flooded areas between the models. Based on the study’s conditions, a result has been achieved where the field study with the measurements has improved the reliability of elevation data and the calibration of the model has been based on well-proven methods in the literature. Further validation has been done against the result of a previous study and it can be concluded that the models are reliable. Like all map production, there are also uncertainties in this study and more studies on other areas are required to determine how a one-dimensional model differs from a two-dimensional one from a distribution perspective. For the prevalence of uncertainty, more studies with 2D data are required to be able to confirm the assumptions made.