摘要: |
Accurate and early information on floods are crucial to ensure transportation safety from flood risk. Bridges and roads can be closed in advance as needed if floods are predicted with sufficient lead time. The Iowa Flood Information System (IFIS) is a state-of-the-art flood forecasting system developed at the Iowa Flood Center (IFC) of the University of Iowa. The platform is based on a rainfall-runoff model and uses data from a wide variety of sources. It monitors floods and also calculates five-day flood risk based on the event return period and duration for over 1000 communities in Iowa. The current system, however, does not account for snow-related processes, which is an important factor for Nebraska. Snowmelt, rain-on-snow events, and frozen soil have significant contributions to the overall flood risk in the state. In the proposed research, the research team plans to improve the hydrologic component of the IFIS by accounting for snow. The team will test different parameterizations for snowmelt and select the one that maintains a good trade-off between performance and complexity. The improved model will be rigorously tested and validated using ground observations. Alongside, the team will be installing automated stream sensors on some selected bridges in the region. The sensors are crucial for real-time monitoring of streamflow. Information collected from these sensors will also be assimilated into the model to improve its performance. The team has chosen the Lower Elkhorn Natural Resources District (LENRD) region for conducting this study because snow-driven flooding is common in this region (e.g. Spring 2019 flood) and also because it is close to Iowa, which simplifies the logistics of this project. Finally, the potential of the platform to be applied across the entire state of Nebraska will be evaluated. |