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.

Earth observation data is increasingly ubiquitous, easily accessible, freely available, and generally usable due to improvements in software, data standards, network infrastructure, and national policies. As a result, greater opportunities arise for using these data in a wider field of application including decision support for local and regional environmental and water resources management efforts. In parts of the world where in situ data are less readily available, global Earth observation data used in such decision support tools can be a boon to underfunded government and private water management agencies. The United Nations Group on Earth Observations Global Water Sustainability initiative (GEOGloWS) works to coordinate such solutions, bringing global water management capabilities to local decision makers. The recent development and deployment of a global hydrologic modelling system based on historical simulations and daily ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) using Earth observations and streamflow routing on every river of the world results in a highly informative and potentially transformative dataset for users at local scales. However, for this data to reach its full potential at the local level, it needs to be subsetted at a regional or local scale, presented in a local geographic context, and interpreted in terms of local water management challenges. Furthermore, this subsetting allows for customization to support the way information is used and the kinds of decisions that are made. This paper presents the design, development, and experimental testing of the GEOGloWS HydroViewer, which is an open source, web-based software that effectively localizes global ECMWF forecasts to meet the needs of water managers and decision makers through subsetting the mapping and modelling services and supporting other customization as needed. The unique Software-as-a-Service (SaaS) deployment method, developed and tested here, allows for individual water management agencies to automatically generate custom HydroViewer applications that can be managed and/or customized depending on need and capacity in-country without reliance on external software and capacity, removing typical interdependence relationships that often define technology transfer to developing countries.

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.

Global hydrological models can fill crucial gaps for providing essential information on water resources management, flood and drought forecasting, and assessing the impacts of climate change. However, these models face several challenges that must be addressed to ensure their applicability at local scales. These challenges include effectively managing Big Data, proper communication, adoption, and achieving accuracy in their results. Achieving accuracy in global hydrological models is critical for acceptance in decision-making, but poses the most significant challenge due to the extensive amount of observed data required and the complexity of obtaining and preparing such data for model evaluation. In this study, I conducted an evaluation of the GEOGloWS ECMWF Streamflow Services (GESS) historical simulation and forecast. The evaluation revealed the presence of systematic biases inherent in global models, which restrict their accuracy and reliability for local applications. To address this limitation, I propose a bias correction methodology that uses local data and employs a quantile-mapping approach to correct the systematic biases in the GESS model. I applied this methodology to the +40 years historical simulation dataset and forecast files released between January 1, 2014, and December 31, 2019, demonstrating its effectiveness in correcting the magnitude and seasonality of simulated streamflow values. Additionally, to enhance communication and adoption of the GESS model, I developed a web application called Historical Validation Tool (HVT) that processes and visualizes observed and simulated historical stream discharge data from the GESS model, performs bias correction on the historical simulation, computes goodness-of-fit metrics, and applies forward bias correction to subsequent forecasts. This web application was customized specifically for Brazil, Colombia, Ecuador, and Peru within the framework of the NASA SERVIR Amazonia Project. HVT enables users from these countries to get adjusted GESS historical simulations and forecasts, enhancing the reliability of GESS modeling results at the local scale. The results demonstrate that the bias correction method significantly improves the accuracy of the GESS historical simulation and forecast, as evidenced by the Kling Gupta Efficiency, making it a valuable tool for hydrological studies and water resources management. Furthermore, HVT with its user-friendly graphical interface, rapid performance, and flood alert capabilities, effectively communicates the improvements in GESS historical and forecasted data.