• Improving Pennsylvania Department of Transportation Hydrologic Disaster Forecasting and Response by Assimilating and Fusing NASA and Other Data Sets
Program Disasters Program
PI / Institution Xu Liang / University of Pittsburgh
Start Date October 1, 2012
End Date December 31, 2013
  • Summary

    The Pennsylvania Department of Transportation (PennDOT) has the primary responsibility for traffic and roadway management for the states entire roadway system. Severe weather-related roadway conditions such as flash floods, snow storms, and icy roads can cause dramatic hydrologic disasters. To protect life and property and, thereby, enhance the states economy, PennDOT is developing its Intelligent Transport System (ITS) to support decision making. PennDOT also plans to incorporate a road weather information system (RWIS) with the ITS Traffic Management Centers (TMCs). However, PennDOTs decision making is fundamentally limited, due to the lack of a hydrologic disaster forecasting system at the road level. The proposed project focuses on improving PennDOTs hydrologic disaster forecasting and response, by using an innovative spatial data fusion and assimilation framework developed by University of Pittsburgh. This framework, using NASA satellite surface soil moisture and snow data, NOAA Next Generation Radar (NEXRAD) precipitation data, and RWIS environmental sensor stations (ESS) data, in conjunction with the NOAH model and the Distributed Hydrology Soil Vegetation Model (DHSVM), will provide effective hydrologic forecasts at the road level to PennDOT ITS, and thus, significantly improve PennDOTs decision-making activities for roadway disaster management. The proposed project is in direct response to NASA solicitation NNH11ZDA001N-DISASTER(A.33 Section 2.1), use NASA Earth science products and information to improve disaster management and policy decisions and is aligned with priority topic, Flood prediction, mapping, analysis, and mitigation. To establish feasibility, we will demonstrate that (1) our innovative data fusion of NASA observations with other data sources at different spatial scales is critical for improving hydrologic forecasting; (2) our innovative data assimilation using NASA observations and RWIS in-situ ESS data can significantly improve the accuracy of model forecasts; and (3) our forecasts are effective and useful for improving PennDOTs decision-making activities.