Improving Hydrologic Disaster Forecasting and Response for Transportation by Assimilating and Fusing NASA and Other Data Sets
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Improving Hydrologic Disaster Forecasting and Response for Transportation by Assimilating and Fusing NASA and Other Data Sets

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    In this 3-year project, the research team developed the Hydrologic Disaster Forecast and Response (HDFR) system, a set of integrated software tools for end users that streamlines hydrologic prediction workflows involving automated retrieval of heterogeneous hydro-meteorological data from multiple sources in near real-time, the computation of critical variables to assess and forecast hydrologic disasters using modern distributed hydrologic model, and data assimilation techniques. The system is intended to be deployed as a decision-support tool in operations where extensive areas need to be monitored for extreme weather events and/or where accurate hydrologic predictions are required. The HDFR has been developed and built as a series of modules grouped under four categories: 1. Data: These modules allow to automatically download information from multiple servers hosted by data providers such as government agencies, comprising meteorological and hydrological observations from land and space-borne sensors, and model predictions such as weather forecasts. 2. Fusion: Enable the combination or “fusing” of observations and/or simulations from different instruments and/or models for generating more accurate estimates. 3. Modeling: Allow the creation of hydrologic models and provide tools to estimate their parameters and initial conditions to maximize the correspondence of the simulations with the observations for improved predictive power. 4. Severity: Contrast current or forecasted conditions with historical observations to assess threat levels and allow for efficient response actions. Most of these modules were incorporated into the Geographic Resources Analysis Support System (GRASS), a popular open-source geographic information system, so that complex simulation workflows (from data acquisition to model result analysis and visualization) can be executed in a unified environment without requiring numerous external tools. Within GRASS, information is organized in a unified place with multiple options for data import and export, and for interoperability between the HDFR’s modules and other general-purpose routines. The research team consisted of researchers from the University of Pittsburgh, Indiana University - Purdue University Indianapolis (IUPUI), and NASA’s Goddard Earth Science Data and Information Services Center (GES-DISC/ADNET). The team also partnered with the Pennsylvania Department of Transportation (PennDOT) for part of the cost sharing and for evaluating the incorporation of the HDFR into transportation infrastructure monitoring operations (for example for the determination of threatened bridges following severe weather). While most of the individual modules of the HDFR were completed and tested, a late start date of the matching fund with PennDOT leads to delays in the full completion of the HDFR’s development. The team will therefore continue the work on the HDFR system throughout 2017. In this report the completed activities of this project for each of the tasks originally identified are described, together with pending activities to be delivered in early 2018.
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