Floods are the most impactful natural disasters on earth, and reliable flood warning systems are critical for disaster preparation, mitigation, and response. The GEOGloWS ECMWF Streamflow Services (GESS) provide forecasted streamflow throughout the world. While forecasted discharge is essential to flood warning, forecasted inundation extents are required to understand and predict flood impact. In this research, I sought to expand GESS flood warning potential by generating inundation extents from streamflow forecasts. I compared Height Above Nearest Drainage (HAND), a method beneficial for flood mapping on a watershed scale, to a 2D hydrodynamic model, specifically Sedimentation and River Hydraulics – Two Dimension (SRH-2D), a method localized to specific areas of high importance. In three study areas in the Amazon basin, I validated HAND and SRH-2D flood maps against water maps derived from satellite SAR imagery. Specifically, I analyzed what features of an SRH-2D model were required to generate more accurate flood extents than HAND. I also analyzed the practicality of using SRH-2D for forecasting by comparing flood extents generated from simulating a complete forecast hydrograph to flood extents precomputed at predetermined, incremental flowrates. The SRH-2D models outperformed HAND, but their accuracy decreased at flowrates different than those used for calibration, limiting their reliability for forecasting and impact analysis. Based on this study, the key features necessary for a reliable SRH-2D model for forecasting include (1) a high-resolution DEM for an accurate representation of the floodplain, (2) correct representation of channel flow control, and (3) a channel bathymetry approximation and exit boundary rating curve that correctly predict water levels at a range of input flowrates. For forecasting practicality, the precomputed flood extents had accuracies comparable to the complete hydrograph simulations, showing their potential for estimating forecasted inundation extents. Future research should include (1) a more comprehensive analysis using existing SRH-2D models in areas with more bathymetry information and calibration data, (2) further assessment of the reliability of precomputed flood maps for forecasting applications, and (3) quantifying the effect of error in the streamflow forecasts on the accuracy of the resulting flood extents.

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.

This diploma thesis deals with the possibilities of using the left inundation area of Svitava river for purposes of transformation of the 100-years flood wave in Svitava. The area of interest is located on the territory of the Holásky and Chrlice city districts. SMS software with a hydraulic two-dimensional simulation model SRH was used to simulate the flow in the current state and variant solutions of the proposed state. Based on the results of the flow in the current state, two variant solutions of the proposed state were designed and simulated. The result of this work is the quantification of the change in the transformation capabilities of the area of interest in both proposed variants and followed by a recommendation.